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

	
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

	
235

	
236

	
237

	
238

	
239

	
240

	
241

	
242

	
243

	
244

	
245

	
246

	
247

	
248

	
249

	
250

	
251

	
252

	
253

	
254

	
255

	
256

	
257

	
258

	
259

	
260

	
261

	
262

	
263

	
264

	
265

	
266

	
267

	
268

	
269

	
270

	
271

	
272

	
273

	
274

	
275

	
276

	
277

	
278

	
279

	
280

	
281

	
282

	
283

	
284

	
285

	
286

	
287

	
288

	
289

	
290

	
291

	
292

	
293

	
294

	
295

	
296

	
297

	
298

	
299

	
300

	
301

	
302

	
303

	
304

	
305

	
306

	
307

	
308

	
309

	
310

	
311

	
312

	
313

	
314

	
315

	
316

	
317

	
318

	
319

	
320

	
321

	
322

	
323

	
324

	
325

	
326

	
327

	
328

	
329

	
330

	
331

	
332

	
333

	
334

	
335

	
336

	
337

	
338

	
339

	
            <!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.utils.module_utils &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">Welcome To SuperGradients</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html">Fill our 4-question quick survey! We will raffle free SuperGradients swag between those who will participate -&gt; Fill Survey</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../welcome.html#supergradients">SuperGradients</a></li>
</ul>
<p class="caption"><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>
<p class="caption"><span class="caption-text">User Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html">What is SuperGradients?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#introducing-the-supergradients-library">Introducing the SuperGradients library</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#installation">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#integrating-your-training-code-complete-walkthrough">Integrating Your Training Code - Complete Walkthrough</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#training-parameters">Training Parameters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#logs-and-checkpoints">Logs and Checkpoints</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#dataset-parameters">Dataset Parameters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#network-architectures">Network Architectures</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#pretrained-models">Pretrained Models</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#how-to-reproduce-our-training-recipes">How To Reproduce Our Training Recipes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#professional-tools-integration">Professional Tools Integration</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide.html#supergradients-faq">SuperGradients FAQ</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.utils.module_utils</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.utils.module_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>


<div class="viewcode-block" id="MultiOutputModule"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.MultiOutputModule">[docs]</a><span class="k">class</span> <span class="nc">MultiOutputModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This module wraps around a container nn.Module (such as Module, Sequential and ModuleList) and allows to extract</span>
<span class="sd">    multiple output from its inner modules on each forward call() (as a list of output tensors)</span>
<span class="sd">    note: the default output of the wrapped module will not be added to the output list by default. To get</span>
<span class="sd">    the default output in the outputs list, explicitly include its path in the @output_paths parameter</span>

<span class="sd">    i.e.</span>
<span class="sd">    for module:</span>
<span class="sd">        Sequential(</span>
<span class="sd">          (0): Sequential(</span>
<span class="sd">            (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)</span>
<span class="sd">            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)</span>
<span class="sd">            (2): ReLU6(inplace=True)</span>
<span class="sd">          )                                         ===================================&gt;&gt;</span>
<span class="sd">          (1): InvertedResidual(</span>
<span class="sd">            (conv): Sequential(</span>
<span class="sd">              (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)</span>
<span class="sd">              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)</span>
<span class="sd">              (2): ReLU6(inplace=True)              ===================================&gt;&gt;</span>
<span class="sd">              (3): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)</span>
<span class="sd">              (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)</span>
<span class="sd">            )</span>
<span class="sd">          )</span>
<span class="sd">        )</span>
<span class="sd">    and paths:</span>
<span class="sd">        [0, [1, &#39;conv&#39;, 2]]</span>
<span class="sd">    the output are marked with arrows</span>
<span class="sd">    &quot;&quot;&quot;</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">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">output_paths</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">prune</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        :param module: The wrapped container module</span>
<span class="sd">        :param output_paths: a list of lists or keys containing the canonical paths to the outputs</span>
<span class="sd">        i.e. [3, [4, &#39;conv&#39;, 5], 7] will extract outputs of layers 3, 7 and 4-&gt;conv-&gt;5</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="bp">self</span><span class="o">.</span><span class="n">output_paths</span> <span class="o">=</span> <span class="n">output_paths</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">module</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_outputs_lists</span> <span class="o">=</span> <span class="p">{}</span>

        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">output_paths</span><span class="p">:</span>
            <span class="n">child</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_recursive</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
            <span class="n">child</span><span class="o">.</span><span class="n">register_forward_hook</span><span class="p">(</span><span class="n">hook</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">save_output_hook</span><span class="p">)</span>

        <span class="c1"># PRUNE THE MODULE TO SUPPORT ALL PROVIDED OUTPUT_PATHS BUT REMOVE ALL REDUNDANT LAYERS</span>
        <span class="k">if</span> <span class="n">prune</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_prune</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">output_paths</span><span class="p">)</span>

<div class="viewcode-block" id="MultiOutputModule.save_output_hook"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.MultiOutputModule.save_output_hook">[docs]</a>    <span class="k">def</span> <span class="nf">save_output_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">output</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_outputs_lists</span><span class="p">[</span><span class="nb">input</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">device</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">)</span></div>

<div class="viewcode-block" id="MultiOutputModule.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.MultiOutputModule.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">x</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_outputs_lists</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">](</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_outputs_lists</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">]</span></div>

    <span class="k">def</span> <span class="nf">_get_recursive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;recursively look for a module using a path&quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
            <span class="k">return</span> <span class="n">module</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">path</span><span class="p">)]</span>
        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">module</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_recursive</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="mi">0</span><span class="p">])],</span> <span class="n">path</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>

    <span class="k">def</span> <span class="nf">_prune</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">output_paths</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Recursively prune the module to support all provided output_paths but remove all redundant layers</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">last_index</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
        <span class="n">last_key</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="c1"># look for the last key from all paths</span>
        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">output_paths</span><span class="p">:</span>
            <span class="n">key</span> <span class="o">=</span> <span class="n">path</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="k">else</span> <span class="n">path</span>
            <span class="n">index</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_modules</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">))</span>
            <span class="k">if</span> <span class="n">index</span> <span class="o">&gt;</span> <span class="n">last_index</span><span class="p">:</span>
                <span class="n">last_index</span> <span class="o">=</span> <span class="n">index</span>
                <span class="n">last_key</span> <span class="o">=</span> <span class="n">key</span>

        <span class="n">module</span><span class="o">.</span><span class="n">_modules</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slice_odict</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_modules</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">last_index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>

        <span class="n">next_level_paths</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">output_paths</span><span class="p">:</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="n">path</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">last_key</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">next_level_paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>

        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">next_level_paths</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_prune</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">last_key</span><span class="p">)],</span> <span class="n">next_level_paths</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_slice_odict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">odict</span><span class="p">:</span> <span class="n">OrderedDict</span><span class="p">,</span> <span class="n">start</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">end</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Slice an OrderedDict in the same logic list,tuple... are sliced&quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="n">OrderedDict</span><span class="p">([</span>
            <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="n">odict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
            <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">odict</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
        <span class="p">])</span></div>


<span class="k">def</span> <span class="nf">_replace_activations_recursive</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">new_activation</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">activations_to_replace</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">type</span><span class="p">]):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    A helper called in replace_activations(...)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="ow">in</span> <span class="n">activations_to_replace</span><span class="p">:</span>
            <span class="nb">setattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">new_activation</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">_replace_activations_recursive</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">new_activation</span><span class="p">,</span> <span class="n">activations_to_replace</span><span class="p">)</span>


<div class="viewcode-block" id="replace_activations"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.replace_activations">[docs]</a><span class="k">def</span> <span class="nf">replace_activations</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">new_activation</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">activations_to_replace</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">type</span><span class="p">]):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Recursively go through module and replaces each activation in activations_to_replace with a copy of new_activation</span>
<span class="sd">    :param module:                  a module that will be changed inplace</span>
<span class="sd">    :param new_activation:          a sample of a new activation (will be copied)</span>
<span class="sd">    :param activations_to_replace:  types of activations to replace, each must be a subclass of nn.Module</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># check arguments once before the recursion</span>
    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">new_activation</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">),</span> <span class="s1">&#39;new_activation should be nn.Module&#39;</span>
    <span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="nb">type</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">activations_to_replace</span><span class="p">]),</span> \
        <span class="s1">&#39;activations_to_replace should be types that are subclasses of nn.Module&#39;</span>

    <span class="c1"># do the replacement</span>
    <span class="n">_replace_activations_recursive</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">new_activation</span><span class="p">,</span> <span class="n">activations_to_replace</span><span class="p">)</span></div>


<div class="viewcode-block" id="fuse_repvgg_blocks_residual_branches"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.fuse_repvgg_blocks_residual_branches">[docs]</a><span class="k">def</span> <span class="nf">fuse_repvgg_blocks_residual_branches</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Call fuse_block_residual_branches for all repvgg blocks in the model</span>
<span class="sd">    :param model: torch.nn.Module with repvgg blocks. Doesn&#39;t have to be entirely consists of repvgg.</span>
<span class="sd">    :type model: torch.nn.Module</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="k">assert</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">training</span><span class="p">,</span> <span class="s2">&quot;To fuse RepVGG block residual branches, model must be on eval mode&quot;</span>
    <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="s1">&#39;fuse_block_residual_branches&#39;</span><span class="p">):</span>
            <span class="n">module</span><span class="o">.</span><span class="n">fuse_block_residual_branches</span><span class="p">()</span>
    <span class="n">model</span><span class="o">.</span><span class="n">build_residual_branches</span> <span class="o">=</span> <span class="kc">False</span></div>


<div class="viewcode-block" id="ConvBNReLU"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.ConvBNReLU">[docs]</a><span class="k">class</span> <span class="nc">ConvBNReLU</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Class for Convolution2d-Batchnorm2d-Relu layer. Default behaviour is Conv-BN-Relu. To exclude Batchnorm module use</span>
<span class="sd">        `use_normalization=False`, to exclude Relu activation use `use_activation=False`.</span>
<span class="sd">    For convolution arguments documentation see `nn.Conv2d`.</span>
<span class="sd">    For batchnorm arguments documentation see `nn.BatchNorm2d`.</span>
<span class="sd">    For relu arguments documentation see `nn.Relu`.</span>
<span class="sd">    &quot;&quot;&quot;</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">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">kernel_size</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span>
                 <span class="n">stride</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
                 <span class="n">padding</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
                 <span class="n">dilation</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
                 <span class="n">groups</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">bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                 <span class="n">padding_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;zeros&#39;</span><span class="p">,</span>
                 <span class="n">use_normalization</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                 <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-5</span><span class="p">,</span>
                 <span class="n">momentum</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">affine</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                 <span class="n">track_running_stats</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                 <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">use_activation</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                 <span class="n">inplace</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="nb">super</span><span class="p">(</span><span class="n">ConvBNReLU</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;conv&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span>
                                              <span class="n">out_channels</span><span class="p">,</span>
                                              <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
                                              <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
                                              <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
                                              <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span>
                                              <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
                                              <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span>
                                              <span class="n">padding_mode</span><span class="o">=</span><span class="n">padding_mode</span><span class="p">))</span>

        <span class="k">if</span> <span class="n">use_normalization</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;bn&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">momentum</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="n">affine</span><span class="p">,</span>
                                                     <span class="n">track_running_stats</span><span class="o">=</span><span class="n">track_running_stats</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
                                                     <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">use_activation</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">))</span>

<div class="viewcode-block" id="ConvBNReLU.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.ConvBNReLU.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">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>


<div class="viewcode-block" id="NormalizationAdapter"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.NormalizationAdapter">[docs]</a><span class="k">class</span> <span class="nc">NormalizationAdapter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Denormalizes input by mean_original, std_original, then normalizes by mean_required, std_required.</span>

<span class="sd">    Used in KD training where teacher expects data normalized by mean_required, std_required.</span>

<span class="sd">    mean_original, std_original, mean_required, std_required are all list-like objects of length that&#39;s equal to the</span>
<span class="sd">     number of input channels.</span>

<span class="sd">    &quot;&quot;&quot;</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">mean_original</span><span class="p">,</span> <span class="n">std_original</span><span class="p">,</span> <span class="n">mean_required</span><span class="p">,</span> <span class="n">std_required</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">NormalizationAdapter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="n">mean_original</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">mean_original</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">std_original</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">std_original</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">mean_required</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">mean_required</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">std_required</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">std_required</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">additive</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">((</span><span class="n">mean_original</span> <span class="o">-</span> <span class="n">mean_required</span><span class="p">)</span> <span class="o">/</span> <span class="n">std_original</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">multiplier</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">std_original</span> <span class="o">/</span> <span class="n">std_required</span><span class="p">)</span>

<div class="viewcode-block" id="NormalizationAdapter.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.module_utils.NormalizationAdapter.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">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">additive</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiplier</span>
        <span class="k">return</span> <span class="n">x</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>