Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git


  
1

	
2

	
3

	
4

	
5

	
6

	
7

	
8

	
9

	
10

	
11

	
12

	
13

	
14

	
15

	
16

	
17

	
18

	
19

	
20

	
21

	
22

	
23

	
24

	
25

	
26

	
27

	
28

	
29

	
30

	
31

	
32

	
33

	
34

	
35

	
36

	
37

	
38

	
39

	
40

	
41

	
42

	
43

	
44

	
45

	
46

	
47

	
48

	
49

	
50

	
51

	
52

	
53

	
54

	
55

	
56

	
57

	
58

	
59

	
60

	
61

	
62

	
63

	
64

	
65

	
66

	
67

	
68

	
69

	
70

	
71

	
72

	
73

	
74

	
75

	
76

	
77

	
78

	
79

	
80

	
81

	
82

	
83

	
84

	
85

	
86

	
87

	
88

	
89

	
90

	
91

	
92

	
93

	
94

	
95

	
96

	
97

	
98

	
99

	
100

	
101

	
102

	
103

	
104

	
105

	
106

	
107

	
108

	
109

	
110

	
111

	
112

	
113

	
114

	
115

	
116

	
117

	
118

	
119

	
120

	
121

	
122

	
123

	
124

	
125

	
126

	
127

	
128

	
129

	
130

	
131

	
132

	
133

	
134

	
135

	
136

	
137

	
138

	
139

	
140

	
141

	
142

	
143

	
144

	
145

	
146

	
147

	
            <!DOCTYPE html>
<html class="writer-html5" lang="en" >
<head>
  <meta charset="utf-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
  <title>super_gradients.training.losses.ddrnet_loss &mdash; SuperGradients 1.0 documentation</title>
      <link rel="stylesheet" href="../../../../_static/pygments.css" type="text/css" />
      <link rel="stylesheet" href="../../../../_static/css/theme.css" type="text/css" />
      <link rel="stylesheet" href="../../../../_static/graphviz.css" type="text/css" />
  <!--[if lt IE 9]>
    <script src="../../../../_static/js/html5shiv.min.js"></script>
  <![endif]-->
  
        <script data-url_root="../../../../" id="documentation_options" src="../../../../_static/documentation_options.js"></script>
        <script src="../../../../_static/jquery.js"></script>
        <script src="../../../../_static/underscore.js"></script>
        <script src="../../../../_static/doctools.js"></script>
    <script src="../../../../_static/js/theme.js"></script>
    <link rel="index" title="Index" href="../../../../genindex.html" />
    <link rel="search" title="Search" href="../../../../search.html" /> 
</head>

<body class="wy-body-for-nav"> 
  <div class="wy-grid-for-nav">
    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
      <div class="wy-side-scroll">
        <div class="wy-side-nav-search" >
            <a href="../../../../index.html" class="icon icon-home"> SuperGradients
          </a>
<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../../../../search.html" method="get">
    <input type="text" name="q" placeholder="Search docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>
        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
              <p class="caption"><span class="caption-text">SuperGradients</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html">SuperGradients</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../super_gradients.common.html">Common</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../super_gradients.training.html">Training</a></li>
</ul>

        </div>
      </div>
    </nav>

    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
          <a href="../../../../index.html">SuperGradients</a>
      </nav>

      <div class="wy-nav-content">
        <div class="rst-content">
          <div role="navigation" aria-label="Page navigation">
  <ul class="wy-breadcrumbs">
      <li><a href="../../../../index.html" class="icon icon-home"></a> &raquo;</li>
          <li><a href="../../../index.html">Module code</a> &raquo;</li>
      <li>super_gradients.training.losses.ddrnet_loss</li>
      <li class="wy-breadcrumbs-aside">
      </li>
  </ul>
  <hr/>
</div>
          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
           <div itemprop="articleBody">
             
  <h1>Source code for super_gradients.training.losses.ddrnet_loss</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.losses.ohem_ce_loss</span> <span class="kn">import</span> <span class="n">OhemCELoss</span>


<div class="viewcode-block" id="DDRNetLoss"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.ddrnet_loss.DDRNetLoss">[docs]</a><span class="k">class</span> <span class="nc">DDRNetLoss</span><span class="p">(</span><span class="n">OhemCELoss</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                 <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.7</span><span class="p">,</span>
                 <span class="n">ohem_percentage</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>
                 <span class="n">weights</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span>
                 <span class="n">ignore_label</span><span class="o">=</span><span class="mi">255</span><span class="p">,</span>
                 <span class="n">num_pixels_exclude_ignored</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        This loss is an extension of the Ohem (Online Hard Example Mining Cross Entropy) Loss.</span>

<span class="sd">        as define in paper:</span>
<span class="sd">        Accurate Semantic Segmentation of Road Scenes ( https://arxiv.org/pdf/2101.06085.pdf )</span>

<span class="sd">        :param threshold: threshold to th hard example mining algorithm</span>
<span class="sd">        :param ohem_percentage: minimum percentage of total pixels for the hard example mining algorithm</span>
<span class="sd">        (taking only the largest) losses</span>
<span class="sd">        :param weights: weights per each input of the loss. This loss supports a multi output (like in DDRNet with</span>
<span class="sd">        an auxiliary head). the losses of each head can be weighted.</span>
<span class="sd">        :param ignore_label: targets label to be ignored</span>
<span class="sd">        :param num_pixels_exclude_ignored: whether to exclude ignore pixels when calculating the mining percentage.</span>
<span class="sd">        see OhemCELoss doc for more details.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span> <span class="n">mining_percent</span><span class="o">=</span><span class="n">ohem_percentage</span><span class="p">,</span> <span class="n">ignore_lb</span><span class="o">=</span><span class="n">ignore_label</span><span class="p">,</span>
                         <span class="n">num_pixels_exclude_ignored</span><span class="o">=</span><span class="n">num_pixels_exclude_ignored</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>

<div class="viewcode-block" id="DDRNetLoss.forward"><a class="viewcode-back" href="../../../../super_gradients.training.losses.html#super_gradients.training.losses.ddrnet_loss.DDRNetLoss.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions_list</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
                <span class="n">targets</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">predictions_list</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
            <span class="n">predictions_list</span> <span class="o">=</span> <span class="p">(</span><span class="n">predictions_list</span><span class="p">,)</span>

        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictions_list</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">),</span> <span class="s2">&quot;num of prediction must be the same as num of loss weights&quot;</span>

        <span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">unweighted_losses</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">weight</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">predictions_list</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">):</span>
            <span class="n">unweighted_loss</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
            <span class="n">unweighted_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">unweighted_loss</span><span class="p">)</span>
            <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">unweighted_loss</span> <span class="o">*</span> <span class="n">weight</span><span class="p">)</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span>
        <span class="n">unweighted_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">total_loss</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">total_loss</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">unweighted_losses</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="o">.</span><span class="n">detach</span><span class="p">()</span></div></div>
</pre></div>

           </div>
          </div>
          <footer>

  <hr/>

  <div role="contentinfo">
    <p>&#169; Copyright 2021, SuperGradients team.</p>
  </div>

  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
    provided by <a href="https://readthedocs.org">Read the Docs</a>.
   

</footer>
        </div>
      </div>
    </section>
  </div>
  <script>
      jQuery(function () {
          SphinxRtdTheme.Navigation.enable(true);
      });
  </script> 

</body>
</html>