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<h1>Source code for torchvision.utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="n">irange</span> <span class="o">=</span> <span class="nb">range</span>
<div class="viewcode-block" id="make_grid"><a class="viewcode-back" href="../../torchvision/utils.html#torchvision.utils.make_grid">[docs]</a><span class="k">def</span> <span class="nf">make_grid</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">nrow</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">normalize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale_each</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pad_value</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Make a grid of images.</span>
<span class="sd"> Args:</span>
<span class="sd"> tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)</span>
<span class="sd"> or a list of images all of the same size.</span>
<span class="sd"> nrow (int, optional): Number of images displayed in each row of the grid.</span>
<span class="sd"> The Final grid size is (B / nrow, nrow). Default is 8.</span>
<span class="sd"> padding (int, optional): amount of padding. Default is 2.</span>
<span class="sd"> normalize (bool, optional): If True, shift the image to the range (0, 1),</span>
<span class="sd"> by subtracting the minimum and dividing by the maximum pixel value.</span>
<span class="sd"> range (tuple, optional): tuple (min, max) where min and max are numbers,</span>
<span class="sd"> then these numbers are used to normalize the image. By default, min and max</span>
<span class="sd"> are computed from the tensor.</span>
<span class="sd"> scale_each (bool, optional): If True, scale each image in the batch of</span>
<span class="sd"> images separately rather than the (min, max) over all images.</span>
<span class="sd"> pad_value (float, optional): Value for the padded pixels.</span>
<span class="sd"> Example:</span>
<span class="sd"> See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span> <span class="ow">or</span>
<span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensor</span><span class="p">))):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'tensor or list of tensors expected, got </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">tensor</span><span class="p">)))</span>
<span class="c1"># if list of tensors, convert to a 4D mini-batch Tensor</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> <span class="c1"># single image H x W</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># single image</span>
<span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="c1"># if single-channel, convert to 3-channel</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">tensor</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">tensor</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="c1"># single-channel images</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">tensor</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">tensor</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">normalize</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> <span class="c1"># avoid modifying tensor in-place</span>
<span class="k">if</span> <span class="nb">range</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">range</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">),</span> \
<span class="s2">"range has to be a tuple (min, max) if specified. min and max are numbers"</span>
<span class="k">def</span> <span class="nf">norm_ip</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="nb">min</span><span class="p">,</span> <span class="nb">max</span><span class="p">):</span>
<span class="n">img</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="nb">min</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="nb">max</span><span class="p">)</span>
<span class="n">img</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="o">-</span><span class="nb">min</span><span class="p">)</span><span class="o">.</span><span class="n">div_</span><span class="p">(</span><span class="nb">max</span> <span class="o">-</span> <span class="nb">min</span> <span class="o">+</span> <span class="mf">1e-5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">norm_range</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="nb">range</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">range</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">norm_ip</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="nb">range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">range</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">norm_ip</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">min</span><span class="p">()),</span> <span class="nb">float</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
<span class="k">if</span> <span class="n">scale_each</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensor</span><span class="p">:</span> <span class="c1"># loop over mini-batch dimension</span>
<span class="n">norm_range</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="nb">range</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">norm_range</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="nb">range</span><span class="p">)</span>
<span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</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">tensor</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="c1"># make the mini-batch of images into a grid</span>
<span class="n">nmaps</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">xmaps</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">nrow</span><span class="p">,</span> <span class="n">nmaps</span><span class="p">)</span>
<span class="n">ymaps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">nmaps</span><span class="p">)</span> <span class="o">/</span> <span class="n">xmaps</span><span class="p">))</span>
<span class="n">height</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">tensor</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">padding</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">padding</span><span class="p">)</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">new_full</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="n">height</span> <span class="o">*</span> <span class="n">ymaps</span> <span class="o">+</span> <span class="n">padding</span><span class="p">,</span> <span class="n">width</span> <span class="o">*</span> <span class="n">xmaps</span> <span class="o">+</span> <span class="n">padding</span><span class="p">),</span> <span class="n">pad_value</span><span class="p">)</span>
<span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">irange</span><span class="p">(</span><span class="n">ymaps</span><span class="p">):</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">irange</span><span class="p">(</span><span class="n">xmaps</span><span class="p">):</span>
<span class="k">if</span> <span class="n">k</span> <span class="o">>=</span> <span class="n">nmaps</span><span class="p">:</span>
<span class="k">break</span>
<span class="n">grid</span><span class="o">.</span><span class="n">narrow</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span> <span class="o">*</span> <span class="n">height</span> <span class="o">+</span> <span class="n">padding</span><span class="p">,</span> <span class="n">height</span> <span class="o">-</span> <span class="n">padding</span><span class="p">)</span>\
<span class="o">.</span><span class="n">narrow</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">x</span> <span class="o">*</span> <span class="n">width</span> <span class="o">+</span> <span class="n">padding</span><span class="p">,</span> <span class="n">width</span> <span class="o">-</span> <span class="n">padding</span><span class="p">)</span>\
<span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">tensor</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">grid</span></div>
<div class="viewcode-block" id="save_image"><a class="viewcode-back" href="../../torchvision/utils.html#torchvision.utils.save_image">[docs]</a><span class="k">def</span> <span class="nf">save_image</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">nrow</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">normalize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale_each</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pad_value</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Save a given Tensor into an image file.</span>
<span class="sd"> Args:</span>
<span class="sd"> tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,</span>
<span class="sd"> saves the tensor as a grid of images by calling ``make_grid``.</span>
<span class="sd"> **kwargs: Other arguments are documented in ``make_grid``.</span>
<span class="sd"> """</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">make_grid</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">nrow</span><span class="o">=</span><span class="n">nrow</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">pad_value</span><span class="o">=</span><span class="n">pad_value</span><span class="p">,</span>
<span class="n">normalize</span><span class="o">=</span><span class="n">normalize</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="nb">range</span><span class="p">,</span> <span class="n">scale_each</span><span class="o">=</span><span class="n">scale_each</span><span class="p">)</span>
<span class="c1"># Add 0.5 after unnormalizing to [0, 255] to round to nearest integer</span>
<span class="n">ndarr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mi">255</span><span class="p">)</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">)</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">ndarr</span><span class="p">)</span>
<span class="n">im</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span></div>
</pre></div>
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