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<h1>Source code for torchreid.utils.model_complexity</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;compute_model_complexity&#39;</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">namedtuple</span><span class="p">,</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">repeat</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="sd">&quot;&quot;&quot;</span>
<span class="sd">Utility</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">_ntuple</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">parse</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">repeat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">return</span> <span class="n">parse</span>
<span class="n">_single</span> <span class="o">=</span> <span class="n">_ntuple</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">_pair</span> <span class="o">=</span> <span class="n">_ntuple</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">_triple</span> <span class="o">=</span> <span class="n">_ntuple</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Convolution</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">hook_convNd</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</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">m</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">cin</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">in_channels</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span><span class="o">*</span><span class="n">cin</span> <span class="c1">#+ (k*cin-1)</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">flops_per_ele</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">/</span> <span class="n">m</span><span class="o">.</span><span class="n">groups</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Pooling</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">hook_maxpool1d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_maxpool2d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</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">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="c1"># ops: compare</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_maxpool3d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</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">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_avgpool1d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_avgpool2d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</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">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_avgpool3d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</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">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_adapmaxpool1d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out_size</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">output_size</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">out_size</span><span class="p">)</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_adapmaxpool2d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out_size</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">output_size</span><span class="p">)</span>
<span class="n">k</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="nb">list</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">:]))</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">out_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_adapmaxpool3d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out_size</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">output_size</span><span class="p">)</span>
<span class="n">k</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="nb">list</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">:]))</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">out_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_adapavgpool1d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out_size</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">output_size</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">out_size</span><span class="p">)</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_adapavgpool2d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out_size</span> <span class="o">=</span> <span class="n">_pair</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">output_size</span><span class="p">)</span>
<span class="n">k</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="nb">list</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">:]))</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">out_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_adapavgpool3d</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out_size</span> <span class="o">=</span> <span class="n">_triple</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">output_size</span><span class="p">)</span>
<span class="n">k</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="nb">list</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">:]))</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">out_size</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">k</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">k</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Non-linear activations</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">hook_relu</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="c1"># eq: max(0, x)</span>
<span class="n">num_ele</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_ele</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_leakyrelu</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="c1"># eq: max(0, x) + negative_slope*min(0, x)</span>
<span class="n">num_ele</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="n">flops</span> <span class="o">=</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">num_ele</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Normalization</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">hook_batchnormNd</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">num_ele</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="n">flops</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">num_ele</span> <span class="c1"># mean and std</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">affine</span><span class="p">:</span>
<span class="n">flops</span> <span class="o">+=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">num_ele</span> <span class="c1"># gamma and beta</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_instancenormNd</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">hook_batchnormNd</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_groupnorm</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">hook_batchnormNd</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hook_layernorm</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">num_ele</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="n">flops</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">num_ele</span> <span class="c1"># mean and std</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">elementwise_affine</span><span class="p">:</span>
<span class="n">flops</span> <span class="o">+=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">num_ele</span> <span class="c1"># gamma and beta</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Linear</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">hook_linear</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">flops_per_ele</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">in_features</span> <span class="c1">#+ (m.in_features-1)</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">flops_per_ele</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_per_ele</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">flops</span><span class="p">)</span>
<span class="n">__generic_flops_counter</span> <span class="o">=</span> <span class="p">{</span>
<span class="c1"># Convolution</span>
<span class="s1">&#39;Conv1d&#39;</span><span class="p">:</span> <span class="n">hook_convNd</span><span class="p">,</span>
<span class="s1">&#39;Conv2d&#39;</span><span class="p">:</span> <span class="n">hook_convNd</span><span class="p">,</span>
<span class="s1">&#39;Conv3d&#39;</span><span class="p">:</span> <span class="n">hook_convNd</span><span class="p">,</span>
<span class="c1"># Pooling</span>
<span class="s1">&#39;MaxPool1d&#39;</span><span class="p">:</span> <span class="n">hook_maxpool1d</span><span class="p">,</span>
<span class="s1">&#39;MaxPool2d&#39;</span><span class="p">:</span> <span class="n">hook_maxpool2d</span><span class="p">,</span>
<span class="s1">&#39;MaxPool3d&#39;</span><span class="p">:</span> <span class="n">hook_maxpool3d</span><span class="p">,</span>
<span class="s1">&#39;AvgPool1d&#39;</span><span class="p">:</span> <span class="n">hook_avgpool1d</span><span class="p">,</span>
<span class="s1">&#39;AvgPool2d&#39;</span><span class="p">:</span> <span class="n">hook_avgpool2d</span><span class="p">,</span>
<span class="s1">&#39;AvgPool3d&#39;</span><span class="p">:</span> <span class="n">hook_avgpool3d</span><span class="p">,</span>
<span class="s1">&#39;AdaptiveMaxPool1d&#39;</span><span class="p">:</span> <span class="n">hook_adapmaxpool1d</span><span class="p">,</span>
<span class="s1">&#39;AdaptiveMaxPool2d&#39;</span><span class="p">:</span> <span class="n">hook_adapmaxpool2d</span><span class="p">,</span>
<span class="s1">&#39;AdaptiveMaxPool3d&#39;</span><span class="p">:</span> <span class="n">hook_adapmaxpool3d</span><span class="p">,</span>
<span class="s1">&#39;AdaptiveAvgPool1d&#39;</span><span class="p">:</span> <span class="n">hook_adapavgpool1d</span><span class="p">,</span>
<span class="s1">&#39;AdaptiveAvgPool2d&#39;</span><span class="p">:</span> <span class="n">hook_adapavgpool2d</span><span class="p">,</span>
<span class="s1">&#39;AdaptiveAvgPool3d&#39;</span><span class="p">:</span> <span class="n">hook_adapavgpool3d</span><span class="p">,</span>
<span class="c1"># Non-linear activations</span>
<span class="s1">&#39;ReLU&#39;</span><span class="p">:</span> <span class="n">hook_relu</span><span class="p">,</span>
<span class="s1">&#39;ReLU6&#39;</span><span class="p">:</span> <span class="n">hook_relu</span><span class="p">,</span>
<span class="s1">&#39;LeakyReLU&#39;</span><span class="p">:</span> <span class="n">hook_leakyrelu</span><span class="p">,</span>
<span class="c1"># Normalization</span>
<span class="s1">&#39;BatchNorm1d&#39;</span><span class="p">:</span> <span class="n">hook_batchnormNd</span><span class="p">,</span>
<span class="s1">&#39;BatchNorm2d&#39;</span><span class="p">:</span> <span class="n">hook_batchnormNd</span><span class="p">,</span>
<span class="s1">&#39;BatchNorm3d&#39;</span><span class="p">:</span> <span class="n">hook_batchnormNd</span><span class="p">,</span>
<span class="s1">&#39;InstanceNorm1d&#39;</span><span class="p">:</span> <span class="n">hook_instancenormNd</span><span class="p">,</span>
<span class="s1">&#39;InstanceNorm2d&#39;</span><span class="p">:</span> <span class="n">hook_instancenormNd</span><span class="p">,</span>
<span class="s1">&#39;InstanceNorm3d&#39;</span><span class="p">:</span> <span class="n">hook_instancenormNd</span><span class="p">,</span>
<span class="s1">&#39;GroupNorm&#39;</span><span class="p">:</span> <span class="n">hook_groupnorm</span><span class="p">,</span>
<span class="s1">&#39;LayerNorm&#39;</span><span class="p">:</span> <span class="n">hook_layernorm</span><span class="p">,</span>
<span class="c1"># Linear</span>
<span class="s1">&#39;Linear&#39;</span><span class="p">:</span> <span class="n">hook_linear</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">__conv_linear_flops_counter</span> <span class="o">=</span> <span class="p">{</span>
<span class="c1"># Convolution</span>
<span class="s1">&#39;Conv1d&#39;</span><span class="p">:</span> <span class="n">hook_convNd</span><span class="p">,</span>
<span class="s1">&#39;Conv2d&#39;</span><span class="p">:</span> <span class="n">hook_convNd</span><span class="p">,</span>
<span class="s1">&#39;Conv3d&#39;</span><span class="p">:</span> <span class="n">hook_convNd</span><span class="p">,</span>
<span class="c1"># Linear</span>
<span class="s1">&#39;Linear&#39;</span><span class="p">:</span> <span class="n">hook_linear</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">_get_flops_counter</span><span class="p">(</span><span class="n">only_conv_linear</span><span class="p">):</span>
<span class="k">if</span> <span class="n">only_conv_linear</span><span class="p">:</span>
<span class="k">return</span> <span class="n">__conv_linear_flops_counter</span>
<span class="k">return</span> <span class="n">__generic_flops_counter</span>
<div class="viewcode-block" id="compute_model_complexity"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.model_complexity.compute_model_complexity">[docs]</a><span class="k">def</span> <span class="nf">compute_model_complexity</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">only_conv_linear</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns number of parameters and FLOPs.</span>
<span class="sd"> .. note::</span>
<span class="sd"> (1) this function only provides an estimate of the theoretical time complexity</span>
<span class="sd"> rather than the actual running time which depends on implementations and hardware,</span>
<span class="sd"> and (2) the FLOPs is only counted for layers that are used at test time. This means</span>
<span class="sd"> that redundant layers such as person ID classification layer will be ignored as it</span>
<span class="sd"> is discarded when doing feature extraction. Note that the inference graph depends on</span>
<span class="sd"> how you construct the computations in ``forward()``.</span>
<span class="sd"> Args:</span>
<span class="sd"> model (nn.Module): network model.</span>
<span class="sd"> input_size (tuple): input size, e.g. (1, 3, 256, 128).</span>
<span class="sd"> verbose (bool, optional): shows detailed complexity of</span>
<span class="sd"> each module. Default is False.</span>
<span class="sd"> only_conv_linear (bool, optional): only considers convolution</span>
<span class="sd"> and linear layers when counting flops. Default is True.</span>
<span class="sd"> If set to False, flops of all layers will be counted.</span>
<span class="sd"> Examples::</span>
<span class="sd"> &gt;&gt;&gt; from torchreid import models, utils</span>
<span class="sd"> &gt;&gt;&gt; model = models.build_model(name=&#39;resnet50&#39;, num_classes=1000)</span>
<span class="sd"> &gt;&gt;&gt; num_params, flops = utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">registered_handles</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">layer_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">&#39;layer&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;class_name&#39;</span><span class="p">,</span> <span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="s1">&#39;flops&#39;</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">_add_hooks</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_has_submodule</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">children</span><span class="p">()))</span><span class="o">&gt;</span><span class="mi">0</span>
<span class="k">def</span> <span class="nf">_hook</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">params</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">flops_counter</span> <span class="o">=</span> <span class="n">_get_flops_counter</span><span class="p">(</span><span class="n">only_conv_linear</span><span class="p">)</span>
<span class="k">if</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">flops_counter</span><span class="p">:</span>
<span class="n">flops</span> <span class="o">=</span> <span class="n">flops_counter</span><span class="p">[</span><span class="n">class_name</span><span class="p">](</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">flops</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">layer_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="n">layer</span><span class="p">(</span>
<span class="n">class_name</span><span class="o">=</span><span class="n">class_name</span><span class="p">,</span>
<span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">,</span>
<span class="n">flops</span><span class="o">=</span><span class="n">flops</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="c1"># only consider the very basic nn layer</span>
<span class="k">if</span> <span class="n">_has_submodule</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="k">return</span>
<span class="n">handle</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">register_forward_hook</span><span class="p">(</span><span class="n">_hook</span><span class="p">)</span>
<span class="n">registered_handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span>
<span class="n">default_train_mode</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">training</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_add_hooks</span><span class="p">)</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">input_size</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
<span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="c1"># forward</span>
<span class="k">for</span> <span class="n">handle</span> <span class="ow">in</span> <span class="n">registered_handles</span><span class="p">:</span>
<span class="n">handle</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">default_train_mode</span><span class="p">)</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="n">per_module_params</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">per_module_flops</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">total_params</span><span class="p">,</span> <span class="n">total_flops</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">layer_list</span><span class="p">:</span>
<span class="n">total_params</span> <span class="o">+=</span> <span class="n">layer</span><span class="o">.</span><span class="n">params</span>
<span class="n">total_flops</span> <span class="o">+=</span> <span class="n">layer</span><span class="o">.</span><span class="n">flops</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="n">per_module_params</span><span class="p">[</span><span class="n">layer</span><span class="o">.</span><span class="n">class_name</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
<span class="n">per_module_flops</span><span class="p">[</span><span class="n">layer</span><span class="o">.</span><span class="n">class_name</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">flops</span><span class="p">)</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="n">num_udscore</span> <span class="o">=</span> <span class="mi">55</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; </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="s1">&#39;-&#39;</span><span class="o">*</span><span class="n">num_udscore</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; Model complexity with input size </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="n">input_size</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; </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="s1">&#39;-&#39;</span><span class="o">*</span><span class="n">num_udscore</span><span class="p">))</span>
<span class="k">for</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">per_module_params</span><span class="p">:</span>
<span class="n">params</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">per_module_params</span><span class="p">[</span><span class="n">class_name</span><span class="p">]))</span>
<span class="n">flops</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">per_module_flops</span><span class="p">[</span><span class="n">class_name</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; </span><span class="si">{}</span><span class="s1"> (params=</span><span class="si">{:,}</span><span class="s1">, flops=</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="n">class_name</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">flops</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; </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="s1">&#39;-&#39;</span><span class="o">*</span><span class="n">num_udscore</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; Total (params=</span><span class="si">{:,}</span><span class="s1">, flops=</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="n">total_params</span><span class="p">,</span> <span class="n">total_flops</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; </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="s1">&#39;-&#39;</span><span class="o">*</span><span class="n">num_udscore</span><span class="p">))</span>
<span class="k">return</span> <span class="n">total_params</span><span class="p">,</span> <span class="n">total_flops</span></div>
</pre></div>
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