add cvpr19 links

AWESOME_REID.html

@@ -195,6 +195,7 @@
 <div class="section" id="cvpr-2019">
 <h3>CVPR 2019<a class="headerlink" href="#cvpr-2019" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
+<li>Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification. [<a class="reference external" href="https://kovenyu.com/papers/2019_CVPR_PEDAL.pdf">paper</a>] [<a class="reference external" href="https://github.com/QizeYang/PAUL">code</a>]</li>
 <li>Joint Discriminative and Generative Learning for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1904.07223">paper</a>][<a class="reference external" href="https://github.com/NVlabs/DG-Net">code</a>]</li>
 <li>Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1904.01990">paper</a>][<a class="reference external" href="https://github.com/zhunzhong07/ECN">code</a>]</li>
 <li>Dissecting Person Re-identification from the Viewpoint of Viewpoint. [<a class="reference external" href="https://arxiv.org/abs/1812.02162">paper</a>][<a class="reference external" href="https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint">code</a>]</li>

_modules/torchreid/data/transforms.html

@@ -311,13 +311,17 @@
 <span class="sd">        norm_std (list): normalization standard deviation values. Default is</span>
 <span class="sd">            ImageNet standard deviation values.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">transforms</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">transforms</span> <span class="o">=</span> <span class="p">[]</span>
+    
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">transforms</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
         <span class="n">transforms</span> <span class="o">=</span> <span class="p">[</span><span class="n">transforms</span><span class="p">]</span>
 
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">transforms</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;transforms must be a list of strings, but found to be </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">transforms</span><span class="p">)))</span>
-
-    <span class="n">transforms</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">transforms</span><span class="p">]</span>
+    
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transforms</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">transforms</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">transforms</span><span class="p">]</span>
     
     <span class="n">normalize</span> <span class="o">=</span> <span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">norm_mean</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="n">norm_std</span><span class="p">)</span>
 

_sources/AWESOME_REID.md.txt

@@ -10,6 +10,7 @@ Here is a collection of ReID-related research with links to papers and code. You
 - **[ArXiv](#arxiv)**
 
 ### CVPR 2019
+- Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification. [[paper](https://kovenyu.com/papers/2019_CVPR_PEDAL.pdf)] [[code](https://github.com/QizeYang/PAUL)]
 - Joint Discriminative and Generative Learning for Person Re-identification. [[paper](https://arxiv.org/abs/1904.07223)][[code](https://github.com/NVlabs/DG-Net)]
 - Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. [[paper](https://arxiv.org/abs/1904.01990)][[code](https://github.com/zhunzhong07/ECN)]
 - Dissecting Person Re-identification from the Viewpoint of Viewpoint. [[paper](https://arxiv.org/abs/1812.02162)][[code](https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint)]