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<title>numpy ์ scipy ๋ฅผ ์ด์ฉํ ํ์ฅ(Extensions) ๋ง๋ค๊ธฐ — PyTorch Tutorials 1.8.1 documentation</title>
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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">Optimizing Vision Transformer Model for Deployment</a></li>
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<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html">Audio manipulation with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#audio-i-o">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#data-augmentation">Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#feature-extractions">Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#feature-augmentation">Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#datasets">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_recognition_with_torchaudio.html">Speech Command Recognition with torchaudio</a></li>
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<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/torchtext_translation.html">TorchText๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
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<p class="caption"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ด์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_conv_bn_fuser.html">(beta) Building a Convolution/Batch Norm fuser in FX</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(์คํ์ฉ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last Memory Format</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch-script-parallelism.html">Dynamic Parallelism in TorchScript</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_autograd.html">Autograd in C++ Frontend</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_classes.html">Extending TorchScript with Custom C++ Classes</a></li>
<li class="toctree-l1"><a class="reference internal" href="dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
</ul>
<p class="caption"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">Profiling your PyTorch Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
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<div class="sphx-glr-example-title section" id="numpy-scipy-extensions">
<span id="sphx-glr-advanced-numpy-extensions-tutorial-py"></span><h1>numpy ์ scipy ๋ฅผ ์ด์ฉํ ํ์ฅ(Extensions) ๋ง๋ค๊ธฐ<a class="headerlink" href="#numpy-scipy-extensions" title="Permalink to this headline">ยถ</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/apaszke">Adam Paszke</a></p>
<p><strong>Updated by</strong>: <a class="reference external" href="https://github.com/adam-dziedzic">Adam Dziedzic</a></p>
<p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/ajin-jng">Ajin Jeong</a></p>
<p>์ด๋ฒ ํํ ๋ฆฌ์ผ์์๋ ๋ ๊ฐ์ง ์์
์ ์ํํ ๊ฒ์
๋๋ค:</p>
<ol class="arabic simple">
<li><dl class="first docutils">
<dt>๋งค๊ฐ ๋ณ์๊ฐ ์๋ ์ ๊ฒฝ๋ง ๊ณ์ธต(layer) ๋ง๋ค๊ธฐ</dt>
<dd><ul class="first last">
<li>์ด๋ ๊ตฌํ์ ์ผ๋ถ๋ก <strong>numpy</strong> ๋ฅผ ํธ์ถํฉ๋๋ค.</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>ํ์ต ๊ฐ๋ฅํ ๊ฐ์ค์น๊ฐ ์๋ ์ ๊ฒฝ๋ง ๊ณ์ธต(layer) ๋ง๋ค๊ธฐ</dt>
<dd><ul class="first last">
<li>์ด๋ ๊ตฌํ์ ์ผ๋ถ๋ก <strong>Scipy</strong> ๋ฅผ ํธ์ถํฉ๋๋ค.</li>
</ul>
</dd>
</dl>
</li>
</ol>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">Function</span>
</pre></div>
</div>
<div class="section" id="parameter-less">
<h2>๋งค๊ฐ ๋ณ์๊ฐ ์๋(Parameter-less) ์์<a class="headerlink" href="#parameter-less" title="Permalink to this headline">ยถ</a></h2>
<p>์ด ๊ณ์ธต(layer)์ ํน๋ณํ ์ ์ฉํ๊ฑฐ๋ ์ํ์ ์ผ๋ก ์ฌ๋ฐ๋ฅธ ์์
์ ์ํํ์ง ์์ต๋๋ค.</p>
<p>์ด๋ฆ์ ๋์ถฉ BadFFTFunction์ผ๋ก ์ง์์ต๋๋ค.</p>
<p><strong>๊ณ์ธต(layer) ๊ตฌํ</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.fft</span> <span class="kn">import</span> <span class="n">rfft2</span><span class="p">,</span> <span class="n">irfft2</span>
<span class="k">class</span> <span class="nc">BadFFTFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="n">numpy_input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="n">result</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">rfft2</span><span class="p">(</span><span class="n">numpy_input</span><span class="p">))</span>
<span class="k">return</span> <span class="nb">input</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
<span class="n">numpy_go</span> <span class="o">=</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">irfft2</span><span class="p">(</span><span class="n">numpy_go</span><span class="p">)</span>
<span class="k">return</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="c1"># ์ด ๊ณ์ธต์๋ ๋งค๊ฐ ๋ณ์๊ฐ ์์ผ๋ฏ๋ก nn.Module ํด๋์ค๊ฐ ์๋ ํจ์๋ก ๊ฐ๋จํ ์ ์ธํ ์ ์์ต๋๋ค.</span>
<span class="k">def</span> <span class="nf">incorrect_fft</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">BadFFTFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>์์ฑ๋ ๊ณ์ธต(layer)์ ์ฌ์ฉ ์์:</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">incorrect_fft</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="n">result</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">size</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 1.1918, 5.1812, 1.8666, 7.5011, 1.1048],
[11.8465, 8.2186, 7.0803, 4.2919, 8.3442],
[ 1.8182, 4.6176, 6.7651, 0.5791, 6.8108],
[ 5.2458, 8.9383, 5.7823, 0.7027, 5.4365],
[11.0041, 5.7957, 5.5234, 5.2831, 0.5729],
[ 5.2458, 5.0415, 12.3049, 0.7973, 5.4365],
[ 1.8182, 6.3979, 5.0856, 8.5986, 6.8108],
[11.8465, 3.9873, 7.0170, 3.8058, 8.3442]],
grad_fn=<BadFFTFunctionBackward>)
tensor([[ 0.7282, -0.8696, -0.2171, -0.1843, 2.2962, 0.7223, 0.0966, -0.2634],
[-0.1332, 0.7575, 1.0439, 0.1442, 1.1827, -0.5091, -0.4563, -1.2774],
[-0.0131, 0.5793, 1.3061, -0.3559, -1.0782, 0.7354, 0.3562, 1.4096],
[ 1.0133, 0.0609, 0.0080, 1.1577, -0.0359, 0.5949, -0.7882, 0.4229],
[-0.7512, 0.1061, 0.6041, 0.7201, -1.0029, 0.7040, 0.5309, 0.7373],
[-1.0538, -0.7516, -0.2200, -0.6258, -0.6360, -0.8461, 0.6220, 0.3452],
[-0.3384, 0.4814, 0.8166, -1.2614, 0.4654, 0.5989, -1.1698, -0.3915],
[-1.2561, -0.8808, -0.0901, 0.3040, -0.2919, -0.7993, -1.4943, -0.4174]],
requires_grad=True)
</pre></div>
</div>
</div>
<div class="section" id="parameterized">
<h2>๋งค๊ฐ ๋ณ์๊ฐ ์๋(Parameterized) ์์<a class="headerlink" href="#parameterized" title="Permalink to this headline">ยถ</a></h2>
<p>๋ฅ๋ฌ๋ ๋ฌธํ์์ ์ด ๊ณ์ธต(layer)์ ์ค์ ์ฐ์ฐ์ ์ํธ ์๊ด(cross-correlation)์ด์ง๋ง
ํฉ์ฑ๊ณฑ(convolution)์ด๋ผ๊ณ ํท๊ฐ๋ฆฌ๊ฒ ๋ถ๋ฅด๊ณ ์์ต๋๋ค.
(ํฉ์ฑ๊ณฑ์ ํํฐ๋ฅผ ๋ค์ง์ด์ ์ฐ์ฐ์ ํ๋ ๋ฐ๋ฉด, ์ํธ ์๊ด์ ๊ทธ๋ ์ง ์์ ์ฐจ์ด๊ฐ ์์ต๋๋ค)</p>
<p>ํ์ต ๊ฐ๋ฅํ ๊ฐ์ค์น๋ฅผ ๊ฐ๋ ํํฐ(์ปค๋)๋ฅผ ๊ฐ๋ ์ํธ ์๊ด ๊ณ์ธต์ ๊ตฌํํด๋ณด๊ฒ ์ต๋๋ค.</p>
<p>์ญ์ ํ ๋จ๊ณ(backward pass)์์๋ ์
๋ ฅ์ ๋ํ ๊ธฐ์ธ๊ธฐ(gradient)์ ํํฐ์ ๋ํ ๊ธฐ์ธ๊ธฐ๋ฅผ ๊ณ์ฐํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">flip</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">convolve2d</span><span class="p">,</span> <span class="n">correlate2d</span>
<span class="kn">from</span> <span class="nn">torch.nn.modules.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">torch.nn.parameter</span> <span class="kn">import</span> <span class="n">Parameter</span>
<span class="k">class</span> <span class="nc">ScipyConv2dFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">bias</span><span class="p">):</span>
<span class="c1"># ๋ถ๋ฆฌ(detach)ํ์ฌ NumPy๋ก ๋ณํ(cast)ํ ์ ์์ต๋๋ค.</span>
<span class="nb">input</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">bias</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span> <span class="nb">filter</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span> <span class="n">bias</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">correlate2d</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="nb">filter</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'valid'</span><span class="p">)</span>
<span class="n">result</span> <span class="o">+=</span> <span class="n">bias</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">input</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
<span class="n">grad_output</span> <span class="o">=</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="nb">input</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">bias</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
<span class="n">grad_output</span> <span class="o">=</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="n">grad_bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">grad_output</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">grad_input</span> <span class="o">=</span> <span class="n">convolve2d</span><span class="p">(</span><span class="n">grad_output</span><span class="p">,</span> <span class="nb">filter</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'full'</span><span class="p">)</span>
<span class="c1"># ์์ค์ ๋ค์๊ณผ ๊ฐ์ด ํํํ ์๋ ์์ต๋๋ค:</span>
<span class="c1"># grad_input = correlate2d(grad_output, flip(flip(filter.numpy(), axis=0), axis=1), mode='full')</span>
<span class="n">grad_filter</span> <span class="o">=</span> <span class="n">correlate2d</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">grad_output</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'valid'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">grad_input</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">grad_filter</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">grad_bias</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">ScipyConv2d</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filter_width</span><span class="p">,</span> <span class="n">filter_height</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ScipyConv2d</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">filter</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">filter_width</span><span class="p">,</span> <span class="n">filter_height</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ScipyConv2dFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>์ฌ์ฉ ์์:</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">module</span> <span class="o">=</span> <span class="n">ScipyConv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Filter and bias: "</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">parameters</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">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">module</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Output from the convolution: "</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span>
<span class="n">output</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Gradient for the input map: "</span><span class="p">,</span> <span class="nb">input</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Filter and bias: [Parameter containing:
tensor([[-0.1336, -0.6881, -0.9291],
[-1.0583, 0.0911, -0.1330],
[ 1.8886, -1.1873, -0.9271]], requires_grad=True), Parameter containing:
tensor([[0.2242]], requires_grad=True)]
Output from the convolution: tensor([[ 1.7079, -4.0808, 4.6885, -3.1957, -1.0276, 0.0479, -3.4685, 0.3095],
[ 4.4948, -1.8569, -2.9479, -1.3499, -1.5769, -3.4808, 2.9621, 3.1992],
[-5.0502, 5.3179, -0.7636, 1.4368, -0.7051, -2.2897, 0.3401, 1.8289],
[ 0.9727, 0.5493, 2.6465, 0.0221, -2.0838, -0.1612, -4.5789, 0.2609],
[ 0.4971, -4.9053, -0.9624, 0.7467, 2.2558, -2.8353, 1.2368, 3.2089],
[-1.9185, 2.7957, -2.2166, 3.5551, -1.5283, 0.8132, -1.5196, 5.6660],
[ 1.5676, 0.2078, -3.5931, -0.5078, 3.6541, 0.8986, 2.2091, -6.6699],
[-2.8834, -6.8470, 1.1085, 5.5331, -1.9636, -2.4588, 1.6258, 2.9292]],
grad_fn=<ScipyConv2dFunctionBackward>)
Gradient for the input map: tensor([[ 0.0343, 0.4980, 1.7687, 1.5384, -0.8241, 1.1944, 2.1080, 0.8530,
2.0764, 0.8278],
[ 0.2705, 2.4755, -1.2543, 0.1837, 1.8013, -2.8876, 2.5897, 2.6227,
1.2479, 0.0673],
[-0.4198, -4.2805, 6.1668, -1.5560, -6.6640, 2.8419, -0.6600, -1.3260,
0.8823, -0.0966],
[ 0.4696, -0.8721, -4.4204, 2.2695, -2.0788, -3.5903, -1.4183, 0.8003,
0.4671, 0.5033],
[-1.9857, 0.5834, -2.2109, -0.3168, 1.2855, 1.4682, 1.8781, 1.3021,
-2.0994, -0.7286],
[ 1.7939, -0.5246, -0.2969, 0.0924, -3.1746, 2.9881, 3.5902, -4.0819,
-0.9623, 0.2196],
[-0.8499, 3.5169, -0.0293, -5.0321, 0.5368, 3.1531, 1.3180, 0.4385,
2.3122, 0.8604],
[ 0.2518, -3.2709, 1.1602, -2.7289, -0.3120, 2.4989, 2.8521, 0.1650,
-0.2918, 1.4658],
[ 0.5459, -1.7471, -5.1379, 2.3841, -1.3956, 1.6900, -1.8936, 1.8379,
2.0487, 1.1009],
[ 1.0530, 0.9579, 2.6557, -3.0724, -4.6226, 1.2529, 2.5129, -4.5211,
1.6708, 1.8171]])
</pre></div>
</div>
<p><strong>๊ธฐ์ธ๊ธฐ(gradient) ํ์ธ:</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.autograd.gradcheck</span> <span class="kn">import</span> <span class="n">gradcheck</span>
<span class="n">moduleConv</span> <span class="o">=</span> <span class="n">ScipyConv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">input</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">gradcheck</span><span class="p">(</span><span class="n">moduleConv</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Are the gradients correct: "</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Are the gradients correct: True
</pre></div>
</div>
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