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 <div class="section" id="using-baidu-warp-ctc-with-mxnet">
 <span id="using-baidu-warp-ctc-with-mxnet"></span><h1>Using Baidu Warp-CTC with MXNet<a class="headerlink" href="#using-baidu-warp-ctc-with-mxnet" title="Permalink to this headline">¶</a></h1>
 <p>Baidu-WarpCTC is a CTC implementation by Baidu that supports using GPU processors. It supports using CTC with LSTM to solve label alignment problems in many areas, such as OCR and speech recognition.</p>
 <p>You can get the source code for the example on <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/warpctc">GitHub</a>.</p>
 <div class="section" id="install-baidu-warp-ctc">
 <span id="install-baidu-warp-ctc"></span><h2>Install Baidu Warp-CTC<a class="headerlink" href="#install-baidu-warp-ctc" title="Permalink to this headline">¶</a></h2>
 <div class="highlight-python"><div class="highlight"><pre><span></span>  cd ~/
   git clone https://github.com/baidu-research/warp-ctc
   cd warp-ctc
   mkdir build
   cd build
   cmake ..
   make
   sudo make install
 </pre></div>
 </div>
 </div>
 <div class="section" id="enable-warp-ctc-in-mxnet">
 <span id="enable-warp-ctc-in-mxnet"></span><h2>Enable Warp-CTC in MXNet<a class="headerlink" href="#enable-warp-ctc-in-mxnet" title="Permalink to this headline">¶</a></h2>
 <div class="highlight-python"><div class="highlight"><pre><span></span>  comment out following lines in make/config.mk
   WARPCTC_PATH = $(HOME)/warp-ctc
   MXNET_PLUGINS += plugin/warpctc/warpctc.mk

   rebuild mxnet by
   make clean &amp;&amp; make -j4
 </pre></div>
 </div>
 </div>
 <div class="section" id="run-examples">
 <span id="run-examples"></span><h2>Run Examples<a class="headerlink" href="#run-examples" title="Permalink to this headline">¶</a></h2>
 <p>There are two examples. One is a toy example that validates CTC integration. The second is an OCR example with LSTM and CTC. You can run it by typing the following code:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span>  cd examples/warpctc
   python lstm_ocr.py
 </pre></div>
 </div>
 <p>The OCR example is constructed as follows:</p>
 <ol class="simple">
 <li>It generates a 80x30-pixel image for a 4-digit captcha using a Python captcha library.</li>
 <li>The 80x30 image is used as 80 input for LSTM, and every input is one column of the image (a 30 dim vector).</li>
 <li>The output layer use CTC loss.</li>
 </ol>
 <p>The following code shows the detailed construction of the net:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span>  <span class="k">def</span> <span class="nf">lstm_unroll</span><span class="p">(</span><span class="n">num_lstm_layer</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span>
                   <span class="n">num_hidden</span><span class="p">,</span> <span class="n">num_label</span><span class="p">):</span>
     <span class="n">param_cells</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="n">last_states</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">num_lstm_layer</span><span class="p">):</span>
         <span class="n">param_cells</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LSTMParam</span><span class="p">(</span><span class="n">i2h_weight</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s2">"l</span><span class="si">%d</span><span class="s2">_i2h_weight"</span> <span class="o">%</span> <span class="n">i</span><span class="p">),</span>
                                      <span class="n">i2h_bias</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s2">"l</span><span class="si">%d</span><span class="s2">_i2h_bias"</span> <span class="o">%</span> <span class="n">i</span><span class="p">),</span>
                                      <span class="n">h2h_weight</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s2">"l</span><span class="si">%d</span><span class="s2">_h2h_weight"</span> <span class="o">%</span> <span class="n">i</span><span class="p">),</span>
                                      <span class="n">h2h_bias</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s2">"l</span><span class="si">%d</span><span class="s2">_h2h_bias"</span> <span class="o">%</span> <span class="n">i</span><span class="p">)))</span>
         <span class="n">state</span> <span class="o">=</span> <span class="n">LSTMState</span><span class="p">(</span><span class="n">c</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s2">"l</span><span class="si">%d</span><span class="s2">_init_c"</span> <span class="o">%</span> <span class="n">i</span><span class="p">),</span>
                           <span class="n">h</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s2">"l</span><span class="si">%d</span><span class="s2">_init_h"</span> <span class="o">%</span> <span class="n">i</span><span class="p">))</span>
         <span class="n">last_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
     <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">last_states</span><span class="p">)</span> <span class="o">==</span> <span class="n">num_lstm_layer</span><span class="p">)</span>
     <span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
     <span class="n">label</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'label'</span><span class="p">)</span>

     <span class="c1">#every column of image is an input, there are seq_len inputs</span>
     <span class="n">wordvec</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">SliceChannel</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="n">seq_len</span><span class="p">,</span> <span class="n">squeeze_axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">hidden_all</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">for</span> <span class="n">seqidx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">seq_len</span><span class="p">):</span>
         <span class="n">hidden</span> <span class="o">=</span> <span class="n">wordvec</span><span class="p">[</span><span class="n">seqidx</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">num_lstm_layer</span><span class="p">):</span>
             <span class="n">next_state</span> <span class="o">=</span> <span class="n">lstm</span><span class="p">(</span><span class="n">num_hidden</span><span class="p">,</span> <span class="n">indata</span><span class="o">=</span><span class="n">hidden</span><span class="p">,</span>
                               <span class="n">prev_state</span><span class="o">=</span><span class="n">last_states</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
                               <span class="n">param</span><span class="o">=</span><span class="n">param_cells</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
                               <span class="n">seqidx</span><span class="o">=</span><span class="n">seqidx</span><span class="p">,</span> <span class="n">layeridx</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
             <span class="n">hidden</span> <span class="o">=</span> <span class="n">next_state</span><span class="o">.</span><span class="n">h</span>
             <span class="n">last_states</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">next_state</span>
         <span class="n">hidden_all</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">hidden</span><span class="p">)</span>
     <span class="n">hidden_concat</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Concat</span><span class="p">(</span><span class="o">*</span><span class="n">hidden_all</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
     <span class="n">pred</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">hidden_concat</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>

     <span class="c1"># here we do NOT need to transpose label as other lstm examples do</span>
     <span class="n">label</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Reshape</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">target_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,))</span>
     <span class="c1">#label should be int type, so use cast</span>
     <span class="n">label</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Cast</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">label</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s1">'int32'</span><span class="p">)</span>
     <span class="n">sm</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">WarpCTC</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">pred</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">label_length</span> <span class="o">=</span> <span class="n">num_label</span><span class="p">,</span> <span class="n">input_length</span> <span class="o">=</span> <span class="n">seq_len</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">sm</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="supporting-multi-label-length">
 <span id="supporting-multi-label-length"></span><h2>Supporting Multi-label Length<a class="headerlink" href="#supporting-multi-label-length" title="Permalink to this headline">¶</a></h2>
 <p>Provide labels with length b. For samples whose label length is smaller than b, append 0 to the label data to make it have length b.</p>
 <p>0 is reserved for a blank label.</p>
 </div>
 <div class="section" id="next-steps">
 <span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
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 <li><a class="reference internal" href="#">Using Baidu Warp-CTC with MXNet</a><ul>
 <li><a class="reference internal" href="#install-baidu-warp-ctc">Install Baidu Warp-CTC</a></li>
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 <li><a class="reference internal" href="#supporting-multi-label-length">Supporting Multi-label Length</a></li>
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