#561 Feature/sg 193 extend output formator

Merged
Ghost merged 1 commits into Deci-AI:master from deci-ai:feature/SG-193-extend_detection_target_transform
  
    
        
          
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

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

          
            <!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.dice_ce_edge_loss &mdash; SuperGradients 3.0.3 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" />
      <link rel="stylesheet" href="../../../../_static/custom.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/_sphinx_javascript_frameworks_compat.js"></script>
        <script src="../../../../_static/doctools.js"></script>
        <script src="../../../../_static/sphinx_highlight.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" role="heading"><span class="caption-text">Welcome To SuperGradients</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html">Version 3 is out! Notebooks have been updated!</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#build-with-supergradients">Build with SuperGradients</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#quick-installation">Quick Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#what-s-new">What’s New</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#coming-soon">Coming soon</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#table-of-content">Table of Content</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#getting-started">Getting Started</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#advanced-features">Advanced Features</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#installation-methods">Installation Methods</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#implemented-model-architectures">Implemented Model Architectures</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#documentation">Documentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#contributing">Contributing</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#citation">Citation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#community">Community</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#license">License</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#deci-platform">Deci Platform</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Technical Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../super_gradients.common.html">Common package</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../super_gradients.training.html">Training package</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.dice_ce_edge_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.dice_ce_edge_loss</h1><div class="highlight"><pre>
<span></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">from</span> <span class="nn">super_gradients.training.losses.dice_loss</span> <span class="kn">import</span> <span class="n">DiceLoss</span><span class="p">,</span> <span class="n">BinaryDiceLoss</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.segmentation_utils</span> <span class="kn">import</span> <span class="n">target_to_binary_edge</span>
<span class="kn">from</span> <span class="nn">torch.nn.modules.loss</span> <span class="kn">import</span> <span class="n">_Loss</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.losses.mask_loss</span> <span class="kn">import</span> <span class="n">MaskAttentionLoss</span>
<div class="viewcode-block" id="DiceCEEdgeLoss"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.losses.DiceCEEdgeLoss">[docs]</a><span class="k">class</span> <span class="nc">DiceCEEdgeLoss</span><span class="p">(</span><span class="n">_Loss</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">num_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">num_aux_heads</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
                 <span class="n">num_detail_heads</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
                 <span class="n">weights</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
                 <span class="n">dice_ce_weights</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
                 <span class="n">ignore_index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span><span class="p">,</span>
                 <span class="n">edge_kernel</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
                 <span class="n">ce_edge_weights</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">.5</span><span class="p">,</span> <span class="mf">.5</span><span class="p">)):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Total loss is computed as follows:</span>
<span class="sd">                Loss-cls-edge = λ1 * CE + λ2 * M * CE , where [λ1, λ2] are ce_edge_weights.</span>
<span class="sd">            For each Main feature maps and auxiliary heads the loss is calculated as:</span>
<span class="sd">                Loss-main-aux = λ3 * Loss-cls-edge + λ4 * Loss-Dice, where [λ3, λ4] are dice_ce_weights.</span>
<span class="sd">            For Feature maps defined as detail maps that predicts only the edge mask, the loss is computed as follow:</span>
<span class="sd">                Loss-detail = BinaryCE + BinaryDice</span>
<span class="sd">            Finally the total loss is computed as follows for the whole feature maps:</span>
<span class="sd">                Loss = Σw[i] * Loss-main-aux[i] + Σw[j] * Loss-detail[j], where `w` is defined as the `weights` argument</span>
<span class="sd">                    `i` in [0, 1 + num_aux_heads], 1 is for the main feature map.</span>
<span class="sd">                    `j` in [1 + num_aux_heads, 1 + num_aux_heads + num_detail_heads].</span>
<span class="sd">        :param num_aux_heads: num of auxiliary heads.</span>
<span class="sd">        :param num_detail_heads: num of detail heads.</span>
<span class="sd">        :param weights: Loss lambda weights.</span>
<span class="sd">        :param dice_ce_weights: weights lambdas between (Dice, CE) losses.</span>
<span class="sd">        :param edge_kernel: kernel size of dilation erosion convolutions for creating the edge feature map.</span>
<span class="sd">        :param ce_edge_weights: weights lambdas between regular CE and edge attention CE.</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="c1"># Check that arguments are valid.</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">==</span> <span class="n">num_aux_heads</span> <span class="o">+</span> <span class="n">num_detail_heads</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>\
            <span class="s2">&quot;Lambda loss weights must be in same size as loss items.&quot;</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">dice_ce_weights</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;dice_ce_weights must an iterable with size 2, found: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dice_ce_weights</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">ce_edge_weights</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;dice_ce_weights must an iterable with size 2, found: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">ce_edge_weights</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">edge_kernel</span> <span class="o">=</span> <span class="n">edge_kernel</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">num_classes</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ignore_index</span> <span class="o">=</span> <span class="n">ignore_index</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dice_ce_weights</span> <span class="o">=</span> <span class="n">dice_ce_weights</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">use_detail</span> <span class="o">=</span> <span class="n">num_detail_heads</span> <span class="o">&gt;</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_aux_heads</span> <span class="o">=</span> <span class="n">num_aux_heads</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_detail_heads</span> <span class="o">=</span> <span class="n">num_detail_heads</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_detail</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">bce</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">binary_dice</span> <span class="o">=</span> <span class="n">BinaryDiceLoss</span><span class="p">(</span><span class="n">apply_sigmoid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ce_edge</span> <span class="o">=</span> <span class="n">MaskAttentionLoss</span><span class="p">(</span>
            <span class="n">criterion</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">(</span><span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="n">ignore_index</span><span class="p">),</span>
            <span class="n">loss_weights</span><span class="o">=</span><span class="n">ce_edge_weights</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dice_loss</span> <span class="o">=</span> <span class="n">DiceLoss</span><span class="p">(</span><span class="n">apply_softmax</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="n">ignore_index</span><span class="p">)</span>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">component_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Component names for logging during training.</span>
<span class="sd">        These correspond to 2nd item in the tuple returned in self.forward(...).</span>
<span class="sd">        See super_gradients.Trainer.train() docs for more info.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;main_loss&quot;</span><span class="p">]</span>
        <span class="c1"># Append aux losses names</span>
        <span class="n">names</span> <span class="o">+=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;aux_loss</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</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="bp">self</span><span class="o">.</span><span class="n">num_aux_heads</span><span class="p">)]</span>
        <span class="c1"># Append detail losses names</span>
        <span class="n">names</span> <span class="o">+=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;detail_loss</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</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="bp">self</span><span class="o">.</span><span class="n">num_detail_heads</span><span class="p">)]</span>
        <span class="n">names</span> <span class="o">+=</span> <span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">]</span>
        <span class="k">return</span> <span class="n">names</span>
<div class="viewcode-block" id="DiceCEEdgeLoss.forward"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.losses.DiceCEEdgeLoss.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">preds</span><span class="p">:</span> <span class="n">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">target</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="sd">&quot;&quot;&quot;</span>
<span class="sd">        :param preds: Model output predictions, must be in the followed format:</span>
<span class="sd">         [Main-feats, Aux-feats[0], ..., Aux-feats[num_auxs-1], Detail-feats[0], ..., Detail-feats[num_details-1]</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_aux_heads</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_detail_heads</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>\
            <span class="sa">f</span><span class="s2">&quot;Wrong num of predictions tensors, expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">num_aux_heads</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_detail_heads</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2"> found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="n">edge_target</span> <span class="o">=</span> <span class="n">target_to_binary_edge</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">edge_kernel</span><span class="p">,</span>
                                            <span class="n">ignore_index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">ignore_index</span><span class="p">,</span> <span class="n">flatten_channels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="c1"># Main and auxiliaries feature maps losses</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="mi">0</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_aux_heads</span><span class="p">):</span>
            <span class="n">ce_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ce_edge</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">target</span><span class="p">,</span> <span class="n">edge_target</span><span class="p">)</span>
            <span class="n">dice_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dice_loss</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">target</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">ce_loss</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dice_ce_weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">dice_loss</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dice_ce_weights</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
            <span class="n">total_loss</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">loss</span>
            <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
        <span class="c1"># Detail feature maps losses</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_detail</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="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_aux_heads</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)):</span>
                <span class="n">bce_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bce</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">edge_target</span><span class="p">)</span>
                <span class="n">dice_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_dice</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">edge_target</span><span class="p">)</span>
                <span class="n">loss</span> <span class="o">=</span> <span class="n">bce_loss</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dice_ce_weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">dice_loss</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dice_ce_weights</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
                <span class="n">total_loss</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">loss</span>
                <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">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">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">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>

          
        
      

  
Tip!
Press p or to see the previous file or, n or to see the next file
Deci-AI / super-gradients connected to https://github.com/Deci-AI/super-gradients.git

#561 Feature/sg 193 extend output formator

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