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<li class="toctree-l1 current"><a class="reference internal" href="modules.html">micromind</a><ul class="current">
<li class="toctree-l2 current"><a class="current reference internal" href="#">micromind package</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#module-micromind.core">micromind.core module</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#micromind.core.Metric"><code class="docutils literal notranslate"><span class="pre">Metric</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#micromind.core.MicroMind"><code class="docutils literal notranslate"><span class="pre">MicroMind</span></code></a></li>
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<section id="micromind-package">
<h1>micromind package<a class="headerlink" href="#micromind-package" title="Link to this heading"></a></h1>
<section id="module-micromind.core">
<span id="micromind-core-module"></span><h2>micromind.core module<a class="headerlink" href="#module-micromind.core" title="Link to this heading"></a></h2>
<p>Core class for micromind. Supports helper function for exports. Out-of-the-box
multi-gpu and FP16 training with HF Accelerate and much more.</p>
<dl class="simple">
<dt>Authors:</dt><dd><ul class="simple">
<li><p>Francesco Paissan, 2023</p></li>
</ul>
</dd>
</dl>
<dl class="py class">
<dt class="sig sig-object py" id="micromind.core.Metric">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">micromind.core.</span></span><span class="sig-name descname"><span class="pre">Metric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_period</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#Metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.Metric" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Class for tracking evaluation metrics during training.</p>
<p>This class allows you to create custom evaluation metrics by providing a
function to compute the metric and specifying a reduction method.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>name</strong> (<em>str</em>) – The name of the metric.</p></li>
<li><p><strong>fn</strong> (<em>Callable</em>) – A function that computes the metric given predictions and batch data.</p></li>
<li><p><strong>reduction</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – The reduction method for the metric (‘sum’ or ‘mean’). Default is ‘mean’.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><ul class="simple">
<li><p><em>Reduced metric. Optionally, you can access the metric history</em></p></li>
<li><p><strong>before call reduce(clear=True)</strong> (<em>torch.Tensor</em>)</p></li>
</ul>
</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">micromind</span> <span class="kn">import</span> <span class="n">Metric</span><span class="p">,</span> <span class="n">Stage</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_metric</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
<span class="gp">... </span> <span class="c1"># Replace this with your custom metric calculation</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">pred</span> <span class="o">-</span> <span class="n">batch</span>
<span class="gp">>>> </span><span class="n">metric</span> <span class="o">=</span> <span class="n">Metric</span><span class="p">(</span><span class="s2">"Custom Metric"</span><span class="p">,</span> <span class="n">custom_metric</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">"mean"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">metric</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">stage</span><span class="o">=</span><span class="n">Stage</span><span class="o">.</span><span class="n">train</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">metric</span><span class="o">.</span><span class="n">history</span>
<span class="go">{0: [tensor([0.5000, 0.5000, 0.5000])], 1: [], 2: []}</span>
<span class="gp">>>> </span><span class="n">metric</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">Stage</span><span class="o">.</span><span class="n">train</span><span class="p">)</span>
<span class="go">0.5</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.Metric.reduce">
<span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clear</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#Metric.reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.Metric.reduce" title="Link to this definition"></a></dt>
<dd><p>Compute and return the metric for a given prediction and batch data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pred</strong> (<em>torch.Tensor</em>) – The model’s prediction.</p></li>
<li><p><strong>batch</strong> (<em>torch.Tensor</em>) – The ground truth or target values.</p></li>
<li><p><strong>stage</strong> (<a class="reference internal" href="#micromind.core.Stage" title="micromind.core.Stage"><em>Stage</em></a>) – The current stage (e.g., Stage.train).</p></li>
<li><p><strong>Optional</strong><strong>[</strong><strong>str</strong><strong>]</strong> (<em>device</em>) – The device on which to perform the computation. Default is ‘cpu’.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="micromind.core.MicroMind">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">micromind.core.</span></span><span class="sig-name descname"><span class="pre">MicroMind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
<p>MicroMind is an abstract base class for creating and training deep learning
models. Handles training on multi-gpu via accelerate (using DDP and other
distributed training strategies). It automatically handles the device
management for the training and the micromind’s export capabilities to onnx,
OpenVino and TFLite.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>hparams</strong> (<em>Optional</em><em>[</em><em>Namespace</em><em>]</em>) – Hyperparameters for the model. Default is None.</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.compute_loss">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.compute_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.compute_loss" title="Link to this definition"></a></dt>
<dd><p>Computes the cost function for the optimization process. It return a
tensor on which backward() is called. This method should be overwritten
for the specific application.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pred</strong> (<em>Union</em><em>[</em><em>torch.Tensor</em><em>, </em><em>Tuple</em><em>]</em>) – Output of the forward() function</p></li>
<li><p><strong>batch</strong> (<em>torch.Tensor</em>) – Batch as defined from the DataLoader.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>loss</strong> – Compute cost function.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>torch.Tensor</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.configure_optimizers">
<span class="sig-name descname"><span class="pre">configure_optimizers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.configure_optimizers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.configure_optimizers" title="Link to this definition"></a></dt>
<dd><p>Configures and defines the optimizer for the task. Defaults to adam
with lr=0.001; It can be overwritten by either passing arguments from the
command line, or by overwriting this entire method.
Scheduler step is called every optimization step.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>: Union[Tuple[torch.optim.Adam, None], torch.optim.Adam]</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>Optimizer and learning rate scheduler.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.eval">
<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.eval" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.export">
<span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Path</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_format</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'onnx'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.export" title="Link to this definition"></a></dt>
<dd><p>Export the model to a specified format for deployment.
TFLite and OpenVINO need a Linux machine to be exported.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>save_dir</strong> (<em>Union</em><em>[</em><em>Path</em><em>, </em><em>str</em><em>]</em>) – The directory where the exported model will be saved.</p></li>
<li><p><strong>out_format</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – The format for exporting the model. Default is ‘onnx’.</p></li>
<li><p><strong>input_shape</strong> (<em>Optional</em><em>[</em><em>Tuple</em><em>]</em>) – The input shape of the model. If not provided, the input shape
specified during model creation is used.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.forward">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.forward" title="Link to this definition"></a></dt>
<dd><p>Forward step of the class. It gets called during inference and optimization.
This method should be overwritten for specific applications.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>batch</strong> (<em>torch.Tensor</em>) – Batch as output from the defined DataLoader.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>pred</strong> – Predictions - this depends on the task.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Union[torch.Tensor, Tuple]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.init_devices">
<span class="sig-name descname"><span class="pre">init_devices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.init_devices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.init_devices" title="Link to this definition"></a></dt>
<dd><p>Initializes the data pipeline and modules for DDP and accelerated inference.
To control the device selection, use <cite>accelerate config</cite>.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.load_modules">
<span class="sig-name descname"><span class="pre">load_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Path</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.load_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.load_modules" title="Link to this definition"></a></dt>
<dd><p>Loads models for path.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>checkpoint_path</strong> (<em>Union</em><em>[</em><em>Path</em><em>, </em><em>str</em><em>]</em>) – Path to the checkpoint where the modules are stored.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.on_train_end">
<span class="sig-name descname"><span class="pre">on_train_end</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.on_train_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.on_train_end" title="Link to this definition"></a></dt>
<dd><p>Runs at the end of each training. Cleans up before exiting.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.on_train_start">
<span class="sig-name descname"><span class="pre">on_train_start</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.on_train_start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.on_train_start" title="Link to this definition"></a></dt>
<dd><p>Initializes the optimizer, modules and puts the networks on the right
devices. Optionally loads checkpoint if already present.</p>
<p>This function gets executed at the beginning of every training.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.set_input_shape">
<span class="sig-name descname"><span class="pre">set_input_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">(3,</span> <span class="pre">224,</span> <span class="pre">224)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.set_input_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.set_input_shape" title="Link to this definition"></a></dt>
<dd><p>Setter function for input_shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>input_shape</strong> (<em>Tuple</em>) – Input shape of the forward step.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.test">
<span class="sig-name descname"><span class="pre">test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metrics</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#micromind.core.Metric" title="micromind.core.Metric"><span class="pre">Metric</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.test" title="Link to this definition"></a></dt>
<dd><p>Runs the test steps.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>datasets</strong> (<em>Dict</em>) – Dictionary with the test DataLoader. Should be present in the key
<cite>test</cite>.</p></li>
<li><p><strong>metrics</strong> (<em>List</em><em>[</em><a class="reference internal" href="#micromind.core.Metric" title="micromind.core.Metric"><em>Metric</em></a><em>]</em>) – List of metrics to compute during test step.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>Metrics computed on test set.</strong></p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Dict[torch.Tensor]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.train">
<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">datasets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metrics</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#micromind.core.Metric" title="micromind.core.Metric"><span class="pre">Metric</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.train" title="Link to this definition"></a></dt>
<dd><p>This method trains the model on the provided training dataset for the
specified number of epochs. It tracks training metrics and can
optionally perform validation during training, if the validation set is
provided.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>epochs</strong> (<em>int</em>) – The number of training epochs.</p></li>
<li><p><strong>datasets</strong> (<em>Dict</em>) – A dictionary of dataset loaders. Dataloader should be mapped to keys
“train”, “val”, and “test”.</p></li>
<li><p><strong>metrics</strong> (<em>Optional</em><em>[</em><em>List</em><em>[</em><a class="reference internal" href="#micromind.core.Metric" title="micromind.core.Metric"><em>Metric</em></a><em>]</em><em>]</em>) – A list of metrics to track during training. Default is an empty list.</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Whether to run in debug mode. Default is False. If in debug mode,
only runs for few epochs
and with few batches.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="micromind.core.MicroMind.validate">
<span class="sig-name descname"><span class="pre">validate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">Dict</span></span></span><a class="reference internal" href="_modules/micromind/core.html#MicroMind.validate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.MicroMind.validate" title="Link to this definition"></a></dt>
<dd><p>Runs the validation step.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="micromind.core.Stage">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">micromind.core.</span></span><span class="sig-name descname"><span class="pre">Stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/core.html#Stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.core.Stage" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>enum to track training stage</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="micromind.core.Stage.test">
<span class="sig-name descname"><span class="pre">test</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">2</span></em><a class="headerlink" href="#micromind.core.Stage.test" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="micromind.core.Stage.train">
<span class="sig-name descname"><span class="pre">train</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0</span></em><a class="headerlink" href="#micromind.core.Stage.train" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="micromind.core.Stage.val">
<span class="sig-name descname"><span class="pre">val</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">1</span></em><a class="headerlink" href="#micromind.core.Stage.val" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</section>
<section id="module-micromind.convert">
<span id="micromind-convert-module"></span><h2>micromind.convert module<a class="headerlink" href="#module-micromind.convert" title="Link to this heading"></a></h2>
<p>Conversion from pytorch to different standard formats
for inference (ONNX, OpenVINO, tflite).</p>
<dl class="simple">
<dt>Authors:</dt><dd><ul class="simple">
<li><p>Francesco Paissan, 2023</p></li>
<li><p>Alberto Ancilotto, 2023</p></li>
</ul>
</dd>
</dl>
<dl class="py function">
<dt class="sig sig-object py" id="micromind.convert.convert_to_onnx">
<span class="sig-prename descclassname"><span class="pre">micromind.convert.</span></span><span class="sig-name descname"><span class="pre">convert_to_onnx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Module</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#micromind.core.MicroMind" title="micromind.core.MicroMind"><span class="pre">MicroMind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Path</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'model.onnx'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">simplify</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace_forward</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/micromind/convert.html#convert_to_onnx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.convert.convert_to_onnx" title="Link to this definition"></a></dt>
<dd><p>Converts nn.Module to onnx and saves it to save_path.
Optionally simplifies it. This function is internally used from <cite>mm.MicroMind</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>net</strong> (<em>Union</em><em>[</em><em>nn.Module</em><em>, </em><em>mm.MicroMind</em><em>]</em>) – PyTorch module to be exported.</p></li>
<li><p><strong>save_path</strong> (<em>Union</em><em>[</em><em>Path</em><em>, </em><em>str</em><em>]</em>) – Output path for the ONNX model.</p></li>
<li><p><strong>simplify</strong> (<em>bool</em>) – <cite>True</cite> if you want to simplify the model. Defaults to False.</p></li>
<li><p><strong>replace_forward</strong> (<em>bool</em>) – Used if you want to replace the forward method. It is need if you are calling
this function on a <cite>mm.MicroMind</cite>. Defaults to False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>The path of the ONNX model.</strong></p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Path</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="micromind.convert.convert_to_openvino">
<span class="sig-prename descclassname"><span class="pre">micromind.convert.</span></span><span class="sig-name descname"><span class="pre">convert_to_openvino</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Module</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#micromind.core.MicroMind" title="micromind.core.MicroMind"><span class="pre">MicroMind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace_forward</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="_modules/micromind/convert.html#convert_to_openvino"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.convert.convert_to_openvino" title="Link to this definition"></a></dt>
<dd><p>Converts model to OpenVINO. Uses ONNX in the process and converts networks
from channel-first to channel-last (for optimized inference).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>net</strong> (<em>nn.Module</em>) – PyTorch module to be exported.</p></li>
<li><p><strong>save_path</strong> (<em>Union</em><em>[</em><em>Path</em><em>, </em><em>str</em><em>]</em>) – Output path for the OpenVINO model.</p></li>
<li><p><strong>replace_forward</strong> (<em>bool</em>) – Used if you want to replace the forward method. It is need if you are calling
this function on a <cite>mm.MicroMind</cite>. Defaults to False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>The path of the XML model.</strong></p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>str</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="micromind.convert.convert_to_tflite">
<span class="sig-prename descclassname"><span class="pre">micromind.convert.</span></span><span class="sig-name descname"><span class="pre">convert_to_tflite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Module</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#micromind.core.MicroMind" title="micromind.core.MicroMind"><span class="pre">MicroMind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Path</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_quant</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace_forward</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/micromind/convert.html#convert_to_tflite"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#micromind.convert.convert_to_tflite" title="Link to this definition"></a></dt>
<dd><p>Converts nn.Module to tf_lite, optionally quantizes it.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>net</strong> (<em>nn.Module</em>) – PyTorch module to be exported.</p></li>
<li><p><strong>save_path</strong> (<em>Union</em><em>[</em><em>Path</em><em>, </em><em>str</em><em>]</em>) – Output path for the OpenVINO model.</p></li>
<li><p><strong>batch_quant</strong> (<em>torch.Tensor</em>) – Optional batch for quantization. When passed, it is used to create the
statistics of the quantized activations.</p></li>
<li><p><strong>replace_forward</strong> (<em>bool</em>) – Used if you want to replace the forward method. It is need if you are calling
this function on a <cite>mm.MicroMind</cite>. Defaults to False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</section>
<section id="subpackages">
<h2>Subpackages<a class="headerlink" href="#subpackages" title="Link to this heading"></a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="micromind.networks.html">micromind.networks package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="micromind.networks.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="micromind.networks.html#module-micromind.networks.phinet">micromind.networks.phinet module</a></li>
<li class="toctree-l2"><a class="reference internal" href="micromind.networks.html#module-micromind.networks.xinet">micromind.networks.xinet module</a></li>
<li class="toctree-l2"><a class="reference internal" href="micromind.networks.html#module-micromind.networks.yolo">micromind.networks.yolo module</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="micromind.utils.html">micromind.utils package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="micromind.utils.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="micromind.utils.html#module-micromind.utils.checkpointer">micromind.utils.checkpointer module</a></li>
<li class="toctree-l2"><a class="reference internal" href="micromind.utils.html#module-micromind.utils.helpers">micromind.utils.helpers module</a></li>
<li class="toctree-l2"><a class="reference internal" href="micromind.utils.html#module-micromind.utils.yolo">micromind.utils.yolo module</a></li>
</ul>
</li>
</ul>
</div>
</section>
</section>
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