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<a class="parent" href="/">home</a>
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<a class="parent" href="index.html">distillation</a>
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<h1>Distilling the Knowledge in a Neural Network</h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper
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<a href="https://papers.labml.ai/paper/1503.02531">Distilling the Knowledge in a Neural Network</a>.</p>
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<p>It’s a way of training a small network using the knowledge in a trained larger network;
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i.e. distilling the knowledge from the large network.</p>
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<p>A large model with regularization or an ensemble of models (using dropout) generalizes
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better than a small model when trained directly on the data and labels.
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However, a small model can be trained to generalize better with help of a large model.
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Smaller models are better in production: faster, less compute, less memory.</p>
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<p>The output probabilities of a trained model give more information than the labels
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because it assigns non-zero probabilities to incorrect classes as well.
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These probabilities tell us that a sample has a chance of belonging to certain classes.
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For instance, when classifying digits, when given an image of digit <em>7</em>,
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a generalized model will give a high probability to 7 and a small but non-zero
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probability to 2, while assigning almost zero probability to other digits.
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Distillation uses this information to train a small model better.</p>
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<h2>Soft Targets</h2>
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<p>The probabilities are usually computed with a softmax operation,</p>
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<p>
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<script type="math/tex; mode=display">q_i = \frac{\exp (z_i)}{\sum_j \exp (z_j)}</script>
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</p>
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<p>where $q_i$ is the probability for class $i$ and $z_i$ is the logit.</p>
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<p>We train the small model to minimize the Cross entropy or KL Divergence between its output
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probability distribution and the large network’s output probability distribution
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(soft targets).</p>
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<p>One of the problems here is that the probabilities assigned to incorrect classes by the
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large network are often very small and don’t contribute to the loss.
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So they soften the probabilities by applying a temperature $T$,</p>
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<p>
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<script type="math/tex; mode=display">q_i = \frac{\exp (\frac{z_i}{T})}{\sum_j \exp (\frac{z_j}{T})}</script>
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</p>
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<p>where higher values for $T$ will produce softer probabilities.</p>
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<h2>Training</h2>
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<p>Paper suggests adding a second loss term for predicting the actual labels
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when training the small model.
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We calculate the composite loss as the weighted sum of the two loss terms:
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soft targets and actual labels.</p>
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<p>The dataset for distillation is called <em>the transfer set</em>, and the paper
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suggests using the same training data.</p>
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<h2>Our experiment</h2>
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<p>We train on CIFAR-10 dataset.
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We <a href="large.html">train a large model</a> that has $14,728,266$ parameters
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with dropout and it gives an accuracy of 85% on the validation set.
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A <a href="small.html">small model</a> with $437,034$ parameters
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gives an accuracy of 80%.</p>
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<p>We then train the small model with distillation from the large model,
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and it gives an accuracy of 82%; a 2% increase in the accuracy.</p>
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<p><a href="https://app.labml.ai/run/d6182e2adaf011eb927c91a2a1710932"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">74</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="lineno">75</span><span class="kn">import</span> <span class="nn">torch.nn.functional</span>
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<span class="lineno">76</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
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<span class="lineno">77</span>
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<span class="lineno">78</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span><span class="p">,</span> <span class="n">tracker</span>
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<span class="lineno">79</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
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<span class="lineno">80</span><span class="kn">from</span> <span class="nn">labml_helpers.train_valid</span> <span class="kn">import</span> <span class="n">BatchIndex</span>
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<span class="lineno">81</span><span class="kn">from</span> <span class="nn">labml_nn.distillation.large</span> <span class="kn">import</span> <span class="n">LargeModel</span>
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<span class="lineno">82</span><span class="kn">from</span> <span class="nn">labml_nn.distillation.small</span> <span class="kn">import</span> <span class="n">SmallModel</span>
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<span class="lineno">83</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-1'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-1'>#</a>
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</div>
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<h2>Configurations</h2>
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<p>This extends from <a href="../experiments/cifar10.html"><code>CIFAR10Configs</code></a> which defines all the
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dataset related configurations, optimizer, and a training loop.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">86</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-2'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-2'>#</a>
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</div>
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<p>The small model</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">94</span> <span class="n">model</span><span class="p">:</span> <span class="n">SmallModel</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-3'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-3'>#</a>
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</div>
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<p>The large model</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">96</span> <span class="n">large</span><span class="p">:</span> <span class="n">LargeModel</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-4'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-4'>#</a>
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</div>
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<p>KL Divergence loss for soft targets</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">98</span> <span class="n">kl_div_loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">KLDivLoss</span><span class="p">(</span><span class="n">log_target</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-5'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-5'>#</a>
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</div>
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<p>Cross entropy loss for true label loss</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">100</span> <span class="n">loss_func</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-6'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-6'>#</a>
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</div>
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<p>Temperature, $T$</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">102</span> <span class="n">temperature</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">5.</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-7'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-7'>#</a>
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</div>
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<p>Weight for soft targets loss.</p>
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<p>The gradients produced by soft targets get scaled by $\frac{1}{T^2}$.
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To compensate for this the paper suggests scaling the soft targets loss
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by a factor of $T^2$</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">108</span> <span class="n">soft_targets_weight</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">100.</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-8'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-8'>#</a>
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</div>
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<p>Weight for true label cross entropy loss</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">110</span> <span class="n">label_loss_weight</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-9'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-9'>#</a>
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</div>
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<h3>Training/validation step</h3>
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<p>We define a custom training/validation step to include the distillation</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">112</span> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="nb">any</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">:</span> <span class="n">BatchIndex</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-10'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-10'>#</a>
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</div>
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<p>Training/Evaluation mode for the small model</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">120</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-11'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-11'>#</a>
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</div>
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<p>Large model in evaluation mode</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">122</span> <span class="bp">self</span><span class="o">.</span><span class="n">large</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-12'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-12'>#</a>
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</div>
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<p>Move data to the device</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">125</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-13'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-13'>#</a>
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</div>
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<p>Update global step (number of samples processed) when in training mode</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">128</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
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<span class="lineno">129</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add_global_step</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-14'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-14'>#</a>
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</div>
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<p>Get the output logits, $v_i$, from the large model</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">132</span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
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<span class="lineno">133</span> <span class="n">large_logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">large</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-15'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-15'>#</a>
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</div>
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<p>Get the output logits, $z_i$, from the small model</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">136</span> <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-16'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-16'>#</a>
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</div>
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<p>Soft targets
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<script type="math/tex; mode=display">p_i = \frac{\exp (\frac{v_i}{T})}{\sum_j \exp (\frac{v_j}{T})}</script>
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</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">140</span> <span class="n">soft_targets</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">large_logits</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-17'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-17'>#</a>
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</div>
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<p>Temperature adjusted probabilities of the small model
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<script type="math/tex; mode=display">q_i = \frac{\exp (\frac{z_i}{T})}{\sum_j \exp (\frac{z_j}{T})}</script>
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</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">143</span> <span class="n">soft_prob</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">output</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-18'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-18'>#</a>
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</div>
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<p>Calculate the soft targets loss</p>
|
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</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">146</span> <span class="n">soft_targets_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kl_div_loss</span><span class="p">(</span><span class="n">soft_prob</span><span class="p">,</span> <span class="n">soft_targets</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-19'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-19'>#</a>
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</div>
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<p>Calculate the true label loss</p>
|
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">148</span> <span class="n">label_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_func</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-20'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-20'>#</a>
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</div>
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<p>Weighted sum of the two losses</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">150</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">soft_targets_weight</span> <span class="o">*</span> <span class="n">soft_targets_loss</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_loss_weight</span> <span class="o">*</span> <span class="n">label_loss</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-21'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-21'>#</a>
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</div>
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<p>Log the losses</p>
|
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">152</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">({</span><span class="s2">"loss.kl_div."</span><span class="p">:</span> <span class="n">soft_targets_loss</span><span class="p">,</span>
|
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<span class="lineno">153</span> <span class="s2">"loss.nll"</span><span class="p">:</span> <span class="n">label_loss</span><span class="p">,</span>
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<span class="lineno">154</span> <span class="s2">"loss."</span><span class="p">:</span> <span class="n">loss</span><span class="p">})</span></pre></div>
|
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</div>
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</div>
|
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<div class='section' id='section-22'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-22'>#</a>
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</div>
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<p>Calculate and log accuracy</p>
|
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">157</span> <span class="bp">self</span><span class="o">.</span><span class="n">accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
|
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<span class="lineno">158</span> <span class="bp">self</span><span class="o">.</span><span class="n">accuracy</span><span class="o">.</span><span class="n">track</span><span class="p">()</span></pre></div>
|
|
</div>
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</div>
|
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<div class='section' id='section-23'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-23'>#</a>
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</div>
|
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<p>Train the model</p>
|
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</div>
|
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<div class='code'>
|
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<div class="highlight"><pre><span class="lineno">161</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span></pre></div>
|
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</div>
|
|
</div>
|
|
<div class='section' id='section-24'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-24'>#</a>
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|
</div>
|
|
<p>Calculate gradients</p>
|
|
</div>
|
|
<div class='code'>
|
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<div class="highlight"><pre><span class="lineno">163</span> <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span></pre></div>
|
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</div>
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</div>
|
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<div class='section' id='section-25'>
|
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<div class='docs'>
|
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<div class='section-link'>
|
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<a href='#section-25'>#</a>
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</div>
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<p>Take optimizer step</p>
|
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</div>
|
|
<div class='code'>
|
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<div class="highlight"><pre><span class="lineno">165</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span></pre></div>
|
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</div>
|
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</div>
|
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<div class='section' id='section-26'>
|
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<div class='docs'>
|
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<div class='section-link'>
|
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<a href='#section-26'>#</a>
|
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</div>
|
|
<p>Log the model parameters and gradients on last batch of every epoch</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">167</span> <span class="k">if</span> <span class="n">batch_idx</span><span class="o">.</span><span class="n">is_last</span><span class="p">:</span>
|
|
<span class="lineno">168</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">'model'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span></pre></div>
|
|
</div>
|
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</div>
|
|
<div class='section' id='section-27'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-27'>#</a>
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</div>
|
|
<p>Clear the gradients</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">170</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-28'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-28'>#</a>
|
|
</div>
|
|
<p>Save the tracked metrics</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">173</span> <span class="n">tracker</span><span class="o">.</span><span class="n">save</span><span class="p">()</span></pre></div>
|
|
</div>
|
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</div>
|
|
<div class='section' id='section-29'>
|
|
<div class='docs doc-strings'>
|
|
<div class='section-link'>
|
|
<a href='#section-29'>#</a>
|
|
</div>
|
|
<h3>Create large model</h3>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">176</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">large</span><span class="p">)</span>
|
|
<span class="lineno">177</span><span class="k">def</span> <span class="nf">_large_model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-30'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-30'>#</a>
|
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</div>
|
|
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">181</span> <span class="k">return</span> <span class="n">LargeModel</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-31'>
|
|
<div class='docs doc-strings'>
|
|
<div class='section-link'>
|
|
<a href='#section-31'>#</a>
|
|
</div>
|
|
<h3>Create small model</h3>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">184</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
|
<span class="lineno">185</span><span class="k">def</span> <span class="nf">_small_student_model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-32'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-32'>#</a>
|
|
</div>
|
|
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">189</span> <span class="k">return</span> <span class="n">SmallModel</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-33'>
|
|
<div class='docs doc-strings'>
|
|
<div class='section-link'>
|
|
<a href='#section-33'>#</a>
|
|
</div>
|
|
<h3>Load <a href="large.html">trained large model</a></h3>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">192</span><span class="k">def</span> <span class="nf">get_saved_model</span><span class="p">(</span><span class="n">run_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-34'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-34'>#</a>
|
|
</div>
|
|
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">197</span> <span class="kn">from</span> <span class="nn">labml_nn.distillation.large</span> <span class="kn">import</span> <span class="n">Configs</span> <span class="k">as</span> <span class="n">LargeConfigs</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-35'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-35'>#</a>
|
|
</div>
|
|
<p>In evaluation mode (no recording)</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">200</span> <span class="n">experiment</span><span class="o">.</span><span class="n">evaluate</span><span class="p">()</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-36'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-36'>#</a>
|
|
</div>
|
|
<p>Initialize configs of the large model training experiment</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">202</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">LargeConfigs</span><span class="p">()</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-37'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-37'>#</a>
|
|
</div>
|
|
<p>Load saved configs</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">204</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="n">experiment</span><span class="o">.</span><span class="n">load_configs</span><span class="p">(</span><span class="n">run_uuid</span><span class="p">))</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-38'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-38'>#</a>
|
|
</div>
|
|
<p>Set models for saving/loading</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">206</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">'model'</span><span class="p">:</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="p">})</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-39'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-39'>#</a>
|
|
</div>
|
|
<p>Set which run and checkpoint to load</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">208</span> <span class="n">experiment</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">run_uuid</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-40'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-40'>#</a>
|
|
</div>
|
|
<p>Start the experiment - this will load the model, and prepare everything</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">210</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">()</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-41'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-41'>#</a>
|
|
</div>
|
|
<p>Return the model</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">213</span> <span class="k">return</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-42'>
|
|
<div class='docs doc-strings'>
|
|
<div class='section-link'>
|
|
<a href='#section-42'>#</a>
|
|
</div>
|
|
<p>Train a small model with distillation</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">216</span><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">run_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-43'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-43'>#</a>
|
|
</div>
|
|
<p>Load saved model</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">221</span> <span class="n">large_model</span> <span class="o">=</span> <span class="n">get_saved_model</span><span class="p">(</span><span class="n">run_uuid</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-44'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-44'>#</a>
|
|
</div>
|
|
<p>Create experiment</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">223</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'distillation'</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">'cifar10'</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-45'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-45'>#</a>
|
|
</div>
|
|
<p>Create configurations</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">225</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-46'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-46'>#</a>
|
|
</div>
|
|
<p>Set the loaded large model</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">227</span> <span class="n">conf</span><span class="o">.</span><span class="n">large</span> <span class="o">=</span> <span class="n">large_model</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-47'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-47'>#</a>
|
|
</div>
|
|
<p>Load configurations</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">229</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span>
|
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<span class="lineno">230</span> <span class="s1">'optimizer.optimizer'</span><span class="p">:</span> <span class="s1">'Adam'</span><span class="p">,</span>
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<span class="lineno">231</span> <span class="s1">'optimizer.learning_rate'</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
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<span class="lineno">232</span> <span class="s1">'model'</span><span class="p">:</span> <span class="s1">'_small_student_model'</span><span class="p">,</span>
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<span class="lineno">233</span> <span class="p">})</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-48'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-48'>#</a>
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</div>
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<p>Set model for saving/loading</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">235</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">'model'</span><span class="p">:</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="p">})</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-49'>
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<div class='docs'>
|
|
<div class='section-link'>
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<a href='#section-49'>#</a>
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</div>
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<p>Start experiment from scratch</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">237</span> <span class="n">experiment</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-50'>
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<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-50'>#</a>
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|
</div>
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<p>Start the experiment and run the training loop</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">239</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
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<span class="lineno">240</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-51'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-51'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">244</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
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<span class="lineno">245</span> <span class="n">main</span><span class="p">(</span><span class="s1">'d46cd53edaec11eb93c38d6538aee7d6'</span><span class="p">,</span> <span class="mi">1_000_000</span><span class="p">)</span></pre></div>
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