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  <h1>Source code for dscript.commands.train</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Train a new model.</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">h5py</span>
<span class="kn">import</span> <span class="nn">datetime</span>
<span class="kn">import</span> <span class="nn">subprocess</span> <span class="k">as</span> <span class="nn">sp</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">gzip</span> <span class="k">as</span> <span class="nn">gz</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">Variable</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">IterableDataset</span><span class="p">,</span> <span class="n">DataLoader</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">average_precision_score</span> <span class="k">as</span> <span class="n">average_precision</span>

<span class="kn">import</span> <span class="nn">dscript</span>
<span class="kn">from</span> <span class="nn">dscript.utils</span> <span class="kn">import</span> <span class="n">PairedDataset</span><span class="p">,</span> <span class="n">collate_paired_sequences</span>
<span class="kn">from</span> <span class="nn">dscript.models.embedding</span> <span class="kn">import</span> <span class="p">(</span>
    <span class="n">IdentityEmbed</span><span class="p">,</span>
    <span class="n">FullyConnectedEmbed</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">dscript.models.contact</span> <span class="kn">import</span> <span class="n">ContactCNN</span>
<span class="kn">from</span> <span class="nn">dscript.models.interaction</span> <span class="kn">import</span> <span class="n">ModelInteraction</span>


<span class="k">def</span> <span class="nf">add_args</span><span class="p">(</span><span class="n">parser</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Create parser for command line utility.</span>

<span class="sd">    :meta private:</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">data_grp</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">add_argument_group</span><span class="p">(</span><span class="s2">&quot;Data&quot;</span><span class="p">)</span>
    <span class="n">proj_grp</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">add_argument_group</span><span class="p">(</span><span class="s2">&quot;Projection Module&quot;</span><span class="p">)</span>
    <span class="n">contact_grp</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">add_argument_group</span><span class="p">(</span><span class="s2">&quot;Contact Module&quot;</span><span class="p">)</span>
    <span class="n">inter_grp</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">add_argument_group</span><span class="p">(</span><span class="s2">&quot;Interaction Module&quot;</span><span class="p">)</span>
    <span class="n">train_grp</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">add_argument_group</span><span class="p">(</span><span class="s2">&quot;Training&quot;</span><span class="p">)</span>
    <span class="n">misc_grp</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">add_argument_group</span><span class="p">(</span><span class="s2">&quot;Output and Device&quot;</span><span class="p">)</span>

    <span class="c1"># Data</span>
    <span class="n">data_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--train&quot;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Training data&quot;</span><span class="p">,</span> <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">data_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--val&quot;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Validation data&quot;</span><span class="p">,</span> <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">data_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--embedding&quot;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;h5 file with embedded sequences&quot;</span><span class="p">,</span> <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">data_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--augment&quot;</span><span class="p">,</span>
        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Set flag to augment data by adding (B A) for all pairs (A B)&quot;</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="c1"># Embedding model</span>
    <span class="n">proj_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--projection-dim&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Dimension of embedding projection layer (default: 100)&quot;</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="n">proj_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--dropout-p&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Parameter p for embedding dropout layer (default: 0.5)&quot;</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="c1"># Contact model</span>
    <span class="n">contact_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--hidden-dim&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Number of hidden units for comparison layer in contact prediction (default: 50)&quot;</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="n">contact_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--kernel-width&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Width of convolutional filter for contact prediction (default: 7)&quot;</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="c1"># Interaction Model</span>
    <span class="n">inter_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--use-w&quot;</span><span class="p">,</span>
        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Use weight matrix in interaction prediction model&quot;</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="n">inter_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--pool-width&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Size of max-pool in interaction model (default: 9)&quot;</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="c1"># Training</span>
    <span class="n">train_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--negative-ratio&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Number of negative training samples for each positive training sample (default: 10)&quot;</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="n">train_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--epoch-scale&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Report heldout performance every this many epochs (default: 5)&quot;</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="n">train_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--num-epochs&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Number of epochs (default: 100)&quot;</span><span class="p">)</span>
    <span class="n">train_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--batch-size&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Minibatch size (default: 25)&quot;</span><span class="p">)</span>
    <span class="n">train_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--weight-decay&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;L2 regularization (default: 0)&quot;</span><span class="p">)</span>
    <span class="n">train_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--lr&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Learning rate (default: 0.001)&quot;</span><span class="p">)</span>
    <span class="n">train_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
        <span class="s2">&quot;--lambda&quot;</span><span class="p">,</span>
        <span class="n">dest</span><span class="o">=</span><span class="s2">&quot;lambda_&quot;</span><span class="p">,</span>
        <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span>
        <span class="n">default</span><span class="o">=</span><span class="mf">0.35</span><span class="p">,</span>
        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Weight on the similarity objective (default: 0.35)&quot;</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="c1"># Output</span>
    <span class="n">misc_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;-o&quot;</span><span class="p">,</span> <span class="s2">&quot;--outfile&quot;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Output file path (default: stdout)&quot;</span><span class="p">)</span>
    <span class="n">misc_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--save-prefix&quot;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Path prefix for saving models&quot;</span><span class="p">)</span>
    <span class="n">misc_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;-d&quot;</span><span class="p">,</span> <span class="s2">&quot;--device&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Compute device to use&quot;</span><span class="p">)</span>
    <span class="n">misc_grp</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;--checkpoint&quot;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Checkpoint model to start training from&quot;</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">parser</span>


<div class="viewcode-block" id="predict_interaction"><a class="viewcode-back" href="../../../api/dscript.commands.html#dscript.commands.train.predict_interaction">[docs]</a><span class="k">def</span> <span class="nf">predict_interaction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Predict whether a list of protein pairs will interact.</span>

<span class="sd">    :param model: Model to be trained</span>
<span class="sd">    :type model: dscript.models.interaction.ModelInteraction</span>
<span class="sd">    :param n0: First protein names</span>
<span class="sd">    :type n0: list[str]</span>
<span class="sd">    :param n1: Second protein names</span>
<span class="sd">    :type n1: list[str]</span>
<span class="sd">    :param tensors: Dictionary of protein names to embeddings</span>
<span class="sd">    :type tensors: dict[str, torch.Tensor]</span>
<span class="sd">    :param use_cuda: Whether to use GPU</span>
<span class="sd">    :type use_cuda: bool</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">b</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">n0</span><span class="p">)</span>

    <span class="n">p_hat</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
        <span class="n">z_a</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="n">n0</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
        <span class="n">z_b</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="n">n1</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
            <span class="n">z_a</span> <span class="o">=</span> <span class="n">z_a</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
            <span class="n">z_b</span> <span class="o">=</span> <span class="n">z_b</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

        <span class="n">p_hat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">z_a</span><span class="p">,</span> <span class="n">z_b</span><span class="p">))</span>
    <span class="n">p_hat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">p_hat</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">p_hat</span></div>


<div class="viewcode-block" id="predict_cmap_interaction"><a class="viewcode-back" href="../../../api/dscript.commands.html#dscript.commands.train.predict_cmap_interaction">[docs]</a><span class="k">def</span> <span class="nf">predict_cmap_interaction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Predict whether a list of protein pairs will interact, as well as their contact map.</span>

<span class="sd">    :param model: Model to be trained</span>
<span class="sd">    :type model: dscript.models.interaction.ModelInteraction</span>
<span class="sd">    :param n0: First protein names</span>
<span class="sd">    :type n0: list[str]</span>
<span class="sd">    :param n1: Second protein names</span>
<span class="sd">    :type n1: list[str]</span>
<span class="sd">    :param tensors: Dictionary of protein names to embeddings</span>
<span class="sd">    :type tensors: dict[str, torch.Tensor]</span>
<span class="sd">    :param use_cuda: Whether to use GPU</span>
<span class="sd">    :type use_cuda: bool</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">b</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">n0</span><span class="p">)</span>

    <span class="n">p_hat</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">c_map_mag</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
        <span class="n">z_a</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="n">n0</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
        <span class="n">z_b</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="n">n1</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
            <span class="n">z_a</span> <span class="o">=</span> <span class="n">z_a</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
            <span class="n">z_b</span> <span class="o">=</span> <span class="n">z_b</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

        <span class="n">cm</span><span class="p">,</span> <span class="n">ph</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">map_predict</span><span class="p">(</span><span class="n">z_a</span><span class="p">,</span> <span class="n">z_b</span><span class="p">)</span>
        <span class="n">p_hat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ph</span><span class="p">)</span>
        <span class="n">c_map_mag</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">cm</span><span class="p">))</span>
    <span class="n">p_hat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">p_hat</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
    <span class="n">c_map_mag</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">c_map_mag</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">c_map_mag</span><span class="p">,</span> <span class="n">p_hat</span></div>


<div class="viewcode-block" id="interaction_grad"><a class="viewcode-back" href="../../../api/dscript.commands.html#dscript.commands.train.interaction_grad">[docs]</a><span class="k">def</span> <span class="nf">interaction_grad</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mf">0.35</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Compute gradient and backpropagate loss for a batch.</span>

<span class="sd">    :param model: Model to be trained</span>
<span class="sd">    :type model: dscript.models.interaction.ModelInteraction</span>
<span class="sd">    :param n0: First protein names</span>
<span class="sd">    :type n0: list[str]</span>
<span class="sd">    :param n1: Second protein names</span>
<span class="sd">    :type n1: list[str]</span>
<span class="sd">    :param y: Interaction labels</span>
<span class="sd">    :type y: torch.Tensor</span>
<span class="sd">    :param tensors: Dictionary of protein names to embeddings</span>
<span class="sd">    :type tensors: dict[str, torch.Tensor]</span>
<span class="sd">    :param use_cuda: Whether to use GPU</span>
<span class="sd">    :type use_cuda: bool</span>
<span class="sd">    :param weight: Weight on the contact map magnitude objective. BCE loss is :math:`1 - \\text{weight}`.</span>
<span class="sd">    :type weight: float</span>

<span class="sd">    :return: (Loss, number correct, mean square error, batch size)</span>
<span class="sd">    :rtype: (torch.Tensor, int, torch.Tensor, int)</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">c_map_mag</span><span class="p">,</span> <span class="n">p_hat</span> <span class="o">=</span> <span class="n">predict_cmap_interaction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>

    <span class="n">bce_loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">p_hat</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="n">y</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>
    <span class="n">cmap_loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">c_map_mag</span><span class="p">)</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="n">weight</span> <span class="o">*</span> <span class="n">bce_loss</span><span class="p">)</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">weight</span><span class="p">)</span> <span class="o">*</span> <span class="n">cmap_loss</span><span class="p">)</span>
    <span class="n">b</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">p_hat</span><span class="p">)</span>

    <span class="c1"># backprop loss</span>
    <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>

    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
        <span class="n">p_hat</span> <span class="o">=</span> <span class="n">p_hat</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</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>
        <span class="n">guess_cutoff</span> <span class="o">=</span> <span class="mf">0.5</span>
        <span class="n">p_hat</span> <span class="o">=</span> <span class="n">p_hat</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">p_guess</span> <span class="o">=</span> <span class="p">(</span><span class="n">guess_cutoff</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">p_hat</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">correct</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">p_guess</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="n">mse</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">-</span> <span class="n">p_hat</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

    <span class="k">return</span> <span class="n">loss</span><span class="p">,</span> <span class="n">correct</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">b</span></div>


<div class="viewcode-block" id="interaction_eval"><a class="viewcode-back" href="../../../api/dscript.commands.html#dscript.commands.train.interaction_eval">[docs]</a><span class="k">def</span> <span class="nf">interaction_eval</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_iterator</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Evaluate test data set performance.</span>

<span class="sd">    :param model: Model to be trained</span>
<span class="sd">    :type model: dscript.models.interaction.ModelInteraction</span>
<span class="sd">    :param test_iterator: Test data iterator</span>
<span class="sd">    :type test_iterator: torch.utils.data.DataLoader</span>
<span class="sd">    :param tensors: Dictionary of protein names to embeddings</span>
<span class="sd">    :type tensors: dict[str, torch.Tensor]</span>
<span class="sd">    :param use_cuda: Whether to use GPU</span>
<span class="sd">    :type use_cuda: bool</span>

<span class="sd">    :return: (Loss, number correct, mean square error, precision, recall, F1 Score, AUPR)</span>
<span class="sd">    :rtype: (torch.Tensor, int, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">p_hat</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">true_y</span> <span class="o">=</span> <span class="p">[]</span>

    <span class="k">for</span> <span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">test_iterator</span><span class="p">:</span>
        <span class="n">p_hat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">predict_interaction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">))</span>
        <span class="n">true_y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>

    <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
    <span class="n">p_hat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">p_hat</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">y</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
        <span class="n">p_hat</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="n">x</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">p_hat</span><span class="p">])</span>
        <span class="n">p_hat</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

    <span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">p_hat</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="n">y</span><span class="o">.</span><span class="n">float</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
    <span class="n">b</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</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>
        <span class="n">guess_cutoff</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="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">p_hat</span> <span class="o">=</span> <span class="n">p_hat</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">p_guess</span> <span class="o">=</span> <span class="p">(</span><span class="n">guess_cutoff</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">p_hat</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">correct</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">p_guess</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="n">mse</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">-</span> <span class="n">p_hat</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

        <span class="n">tp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">p_hat</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="n">pr</span> <span class="o">=</span> <span class="n">tp</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">p_hat</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="n">re</span> <span class="o">=</span> <span class="n">tp</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="n">f1</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">pr</span> <span class="o">*</span> <span class="n">re</span> <span class="o">/</span> <span class="p">(</span><span class="n">pr</span> <span class="o">+</span> <span class="n">re</span><span class="p">)</span>

    <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
    <span class="n">p_hat</span> <span class="o">=</span> <span class="n">p_hat</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>

    <span class="n">aupr</span> <span class="o">=</span> <span class="n">average_precision</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">p_hat</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">loss</span><span class="p">,</span> <span class="n">correct</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">pr</span><span class="p">,</span> <span class="n">re</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">aupr</span></div>


<span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Run training from arguments.</span>

<span class="sd">    :meta private:</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">output</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">outfile</span>
    <span class="k">if</span> <span class="n">output</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">output</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;# Called as: </span><span class="si">{</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">output</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Called as: </span><span class="si">{</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>

    <span class="c1"># Set device</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">device</span>
    <span class="n">use_cuda</span> <span class="o">=</span> <span class="p">(</span><span class="n">device</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;# Using CUDA device </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s2"> - </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_name</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
            <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">,</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Using CPU&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span>

    <span class="n">batch_size</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">batch_size</span>

    <span class="n">train_fi</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">train</span>
    <span class="n">test_fi</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">val</span>
    <span class="n">augment</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">augment</span>
    <span class="n">embedding_h5</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">embedding</span>
    <span class="n">h5fi</span> <span class="o">=</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">embedding_h5</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Loading training pairs from </span><span class="si">{</span><span class="n">train_fi</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

    <span class="n">train_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">train_fi</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">augment</span><span class="p">:</span>
        <span class="n">train_n0</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">train_df</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">train_df</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">train_n1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">train_df</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">train_df</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">train_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">train_df</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">train_df</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">train_n0</span><span class="p">,</span> <span class="n">train_n1</span> <span class="o">=</span> <span class="n">train_df</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">train_df</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">train_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">train_df</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Loading testing pairs from </span><span class="si">{</span><span class="n">test_fi</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

    <span class="n">test_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">test_fi</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">augment</span><span class="p">:</span>
        <span class="n">test_n0</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">test_df</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_df</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">test_n1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">test_df</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">test_df</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">test_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">test_df</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">test_df</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">test_n0</span><span class="p">,</span> <span class="n">test_n1</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_df</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">test_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">test_df</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
    <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

    <span class="n">train_pairs</span> <span class="o">=</span> <span class="n">PairedDataset</span><span class="p">(</span><span class="n">train_n0</span><span class="p">,</span> <span class="n">train_n1</span><span class="p">,</span> <span class="n">train_y</span><span class="p">)</span>
    <span class="n">pairs_train_iterator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
        <span class="n">train_pairs</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
        <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate_paired_sequences</span><span class="p">,</span>
        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="n">test_pairs</span> <span class="o">=</span> <span class="n">PairedDataset</span><span class="p">(</span><span class="n">test_n0</span><span class="p">,</span> <span class="n">test_n1</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>
    <span class="n">pairs_test_iterator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
        <span class="n">test_pairs</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
        <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate_paired_sequences</span><span class="p">,</span>
        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Loading embeddings&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="n">tensors</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="n">all_proteins</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">train_n0</span><span class="p">)</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">train_n1</span><span class="p">))</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_n0</span><span class="p">))</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_n1</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">prot_name</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">all_proteins</span><span class="p">):</span>
        <span class="n">tensors</span><span class="p">[</span><span class="n">prot_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">h5fi</span><span class="p">[</span><span class="n">prot_name</span><span class="p">][:,</span> <span class="p">:])</span>

    <span class="n">use_cuda</span> <span class="o">=</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">checkpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>

        <span class="n">projection_dim</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">projection_dim</span>
        <span class="n">dropout_p</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">dropout_p</span>
        <span class="n">embedding</span> <span class="o">=</span> <span class="n">FullyConnectedEmbed</span><span class="p">(</span><span class="mi">6165</span><span class="p">,</span> <span class="n">projection_dim</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout_p</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Initializing embedding model with:&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">projection_dim: </span><span class="si">{</span><span class="n">projection_dim</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">dropout_p: </span><span class="si">{</span><span class="n">dropout_p</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>

        <span class="c1"># Create contact model</span>
        <span class="n">hidden_dim</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">hidden_dim</span>
        <span class="n">kernel_width</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">kernel_width</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Initializing contact model with:&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">hidden_dim: </span><span class="si">{</span><span class="n">hidden_dim</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">kernel_width: </span><span class="si">{</span><span class="n">kernel_width</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>

        <span class="n">contact</span> <span class="o">=</span> <span class="n">ContactCNN</span><span class="p">(</span><span class="n">projection_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">kernel_width</span><span class="p">)</span>

        <span class="c1"># Create the full model</span>
        <span class="n">use_W</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">use_w</span>
        <span class="n">pool_width</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">pool_width</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Initializing interaction model with:&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">pool_width: </span><span class="si">{</span><span class="n">pool_width</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">use_w: </span><span class="si">{</span><span class="n">use_W</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">ModelInteraction</span><span class="p">(</span><span class="n">embedding</span><span class="p">,</span> <span class="n">contact</span><span class="p">,</span> <span class="n">use_W</span><span class="o">=</span><span class="n">use_W</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="n">pool_width</span><span class="p">)</span>

        <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>

    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Loading model from checkpoint </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">checkpoint</span><span class="p">),</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">checkpoint</span><span class="p">)</span>
        <span class="n">model</span><span class="o">.</span><span class="n">use_cuda</span> <span class="o">=</span> <span class="n">use_cuda</span>

    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

    <span class="c1"># Train the model</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">lr</span>
    <span class="n">wd</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">weight_decay</span>
    <span class="n">num_epochs</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">num_epochs</span>
    <span class="n">batch_size</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">batch_size</span>
    <span class="n">report_steps</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">epoch_scale</span>
    <span class="n">inter_weight</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">lambda_</span>
    <span class="n">cmap_weight</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">inter_weight</span>
    <span class="n">digits</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">)))</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">save_prefix</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">save_prefix</span>
    <span class="k">if</span> <span class="n">save_prefix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">save_prefix</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2">-%H-%M&quot;</span><span class="p">)</span>

    <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">]</span>
    <span class="n">optim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="n">wd</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;# Using save prefix &quot;</span><span class="si">{</span><span class="n">save_prefix</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Training with Adam: lr=</span><span class="si">{</span><span class="n">lr</span><span class="si">}</span><span class="s2">, weight_decay=</span><span class="si">{</span><span class="n">wd</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">num_epochs: </span><span class="si">{</span><span class="n">num_epochs</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">epoch_scale: </span><span class="si">{</span><span class="n">report_steps</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">batch_size: </span><span class="si">{</span><span class="n">batch_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">interaction weight: </span><span class="si">{</span><span class="n">inter_weight</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">contact map weight: </span><span class="si">{</span><span class="n">cmap_weight</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
    <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

    <span class="n">batch_report_fmt</span> <span class="o">=</span> <span class="s2">&quot;# [</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">] training </span><span class="si">{:.1%}</span><span class="s2">: Loss=</span><span class="si">{:.6}</span><span class="s2">, Accuracy=</span><span class="si">{:.3%}</span><span class="s2">, MSE=</span><span class="si">{:.6}</span><span class="s2">&quot;</span>
    <span class="n">epoch_report_fmt</span> <span class="o">=</span> <span class="s2">&quot;# Finished Epoch </span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">: Loss=</span><span class="si">{:.6}</span><span class="s2">, Accuracy=</span><span class="si">{:.3%}</span><span class="s2">, MSE=</span><span class="si">{:.6}</span><span class="s2">, Precision=</span><span class="si">{:.6}</span><span class="s2">, Recall=</span><span class="si">{:.6}</span><span class="s2">, F1=</span><span class="si">{:.6}</span><span class="s2">, AUPR=</span><span class="si">{:.6}</span><span class="s2">&quot;</span>

    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pairs_train_iterator</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_size</span>
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">):</span>

        <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>

        <span class="n">n</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">loss_accum</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">acc_accum</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">mse_accum</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="c1"># Train batches</span>
        <span class="k">for</span> <span class="p">(</span><span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">pairs_train_iterator</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">num_epochs</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span><span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">pairs_train_iterator</span><span class="p">)):</span>

            <span class="n">loss</span><span class="p">,</span> <span class="n">correct</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">interaction_grad</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">inter_weight</span><span class="p">)</span>

            <span class="n">n</span> <span class="o">+=</span> <span class="n">b</span>
            <span class="n">delta</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="p">(</span><span class="n">loss</span> <span class="o">-</span> <span class="n">loss_accum</span><span class="p">)</span>
            <span class="n">loss_accum</span> <span class="o">+=</span> <span class="n">delta</span> <span class="o">/</span> <span class="n">n</span>

            <span class="n">delta</span> <span class="o">=</span> <span class="n">correct</span> <span class="o">-</span> <span class="n">b</span> <span class="o">*</span> <span class="n">acc_accum</span>
            <span class="n">acc_accum</span> <span class="o">+=</span> <span class="n">delta</span> <span class="o">/</span> <span class="n">n</span>

            <span class="n">delta</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="p">(</span><span class="n">mse</span> <span class="o">-</span> <span class="n">mse_accum</span><span class="p">)</span>
            <span class="n">mse_accum</span> <span class="o">+=</span> <span class="n">delta</span> <span class="o">/</span> <span class="n">n</span>

            <span class="n">report</span> <span class="o">=</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">b</span><span class="p">)</span> <span class="o">//</span> <span class="mi">100</span> <span class="o">&lt;</span> <span class="n">n</span> <span class="o">//</span> <span class="mi">100</span>

            <span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
            <span class="n">optim</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            <span class="n">model</span><span class="o">.</span><span class="n">clip</span><span class="p">()</span>

            <span class="k">if</span> <span class="n">report</span><span class="p">:</span>
                <span class="n">tokens</span> <span class="o">=</span> <span class="p">[</span>
                    <span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
                    <span class="n">num_epochs</span><span class="p">,</span>
                    <span class="n">n</span> <span class="o">/</span> <span class="n">N</span><span class="p">,</span>
                    <span class="n">loss_accum</span><span class="p">,</span>
                    <span class="n">acc_accum</span><span class="p">,</span>
                    <span class="n">mse_accum</span><span class="p">,</span>
                <span class="p">]</span>
                <span class="k">if</span> <span class="n">output</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">:</span>
                    <span class="nb">print</span><span class="p">(</span><span class="n">batch_report_fmt</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">tokens</span><span class="p">),</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
                    <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

        <span class="k">if</span> <span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">report_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</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>

                <span class="p">(</span>
                    <span class="n">inter_loss</span><span class="p">,</span>
                    <span class="n">inter_correct</span><span class="p">,</span>
                    <span class="n">inter_mse</span><span class="p">,</span>
                    <span class="n">inter_pr</span><span class="p">,</span>
                    <span class="n">inter_re</span><span class="p">,</span>
                    <span class="n">inter_f1</span><span class="p">,</span>
                    <span class="n">inter_aupr</span><span class="p">,</span>
                <span class="p">)</span> <span class="o">=</span> <span class="n">interaction_eval</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">pairs_test_iterator</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">)</span>
                <span class="n">tokens</span> <span class="o">=</span> <span class="p">[</span>
                    <span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
                    <span class="n">num_epochs</span><span class="p">,</span>
                    <span class="n">inter_loss</span><span class="p">,</span>
                    <span class="n">inter_correct</span> <span class="o">/</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pairs_test_iterator</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">),</span>
                    <span class="n">inter_mse</span><span class="p">,</span>
                    <span class="n">inter_pr</span><span class="p">,</span>
                    <span class="n">inter_re</span><span class="p">,</span>
                    <span class="n">inter_f1</span><span class="p">,</span>
                    <span class="n">inter_aupr</span><span class="p">,</span>
                <span class="p">]</span>
                <span class="nb">print</span><span class="p">(</span><span class="n">epoch_report_fmt</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">tokens</span><span class="p">),</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
                <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

            <span class="c1"># Save the model</span>
            <span class="k">if</span> <span class="n">save_prefix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">save_path</span> <span class="o">=</span> <span class="n">save_prefix</span> <span class="o">+</span> <span class="s2">&quot;_epoch&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="n">digits</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.sav&quot;</span>
                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Saving model to </span><span class="si">{</span><span class="n">save_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
                <span class="n">model</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">save_path</span><span class="p">)</span>
                <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
                    <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

        <span class="n">output</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>

    <span class="k">if</span> <span class="n">save_prefix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">save_path</span> <span class="o">=</span> <span class="n">save_prefix</span> <span class="o">+</span> <span class="s2">&quot;_final.sav&quot;</span>
        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Saving final model to </span><span class="si">{</span><span class="n">save_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">output</span><span class="p">)</span>
        <span class="n">model</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">save_path</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
            <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

    <span class="n">output</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>


<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
    <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="vm">__doc__</span><span class="p">)</span>
    <span class="n">add_args</span><span class="p">(</span><span class="n">parser</span><span class="p">)</span>
    <span class="n">main</span><span class="p">(</span><span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">())</span>
</pre></div>

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