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<title>numpy ์ scipy ๋ฅผ ์ด์ฉํ ํ์ฅ(Extensions) ๋ง๋ค๊ธฐ — PyTorch Tutorials 1.9.0+cu102 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) ์์ํ๊ธฐ</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/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ Dataloader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</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/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>
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<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#resampling">Resampling</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_tutorial.html">Speech Command Recognition with torchaudio</a></li>
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<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<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/translation_transformer.html">Language Translation with nn.Transformer and torchtext</a></li>
</ul>
<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<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>
</ul>
<p class="caption"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ 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>
<li class="toctree-l1"><a class="reference internal" href="super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<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>
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๊ฐ๋ฐํ๊ธฐ</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([[ 6.5785, 16.9776, 12.3807, 3.9286, 0.9177],
[12.1542, 8.1604, 1.9630, 5.1761, 6.7112],
[ 8.7236, 3.4888, 6.1742, 9.1973, 2.0199],
[ 9.9759, 10.2111, 3.1523, 4.0119, 10.3972],
[13.3308, 5.4165, 6.0976, 8.8521, 10.7012],
[ 9.9759, 1.7258, 7.5014, 7.7141, 10.3972],
[ 8.7236, 4.9188, 3.3231, 9.0297, 2.0199],
[12.1542, 22.9124, 6.0236, 1.1640, 6.7112]],
grad_fn=<BadFFTFunctionBackward>)
tensor([[-1.4827, -0.2313, -1.0591, -0.2327, 0.3606, 3.0922, -0.6089, 0.4356],
[ 0.6002, -0.8508, -0.3500, -0.3262, 0.7933, 0.3310, 1.3764, 0.6070],
[ 0.1961, 0.0196, -0.5112, 0.3239, 0.5684, 0.1654, -1.6776, 0.2994],
[ 1.3552, -0.1995, 1.0925, 0.9909, -1.6345, -0.7986, -0.9401, -0.8252],
[-0.5567, -0.0519, -0.4959, -0.3901, 2.1728, -0.0453, -0.8187, -1.5958],
[-0.7908, 0.0720, 0.0153, -0.3254, 1.2588, -0.1634, -1.7394, -1.4636],
[-1.4054, -1.6170, -1.6644, -0.7808, 0.0154, -0.0046, -0.9146, -1.4591],
[ 0.7453, -0.9870, -1.1437, 1.8105, 1.6656, 2.1833, 1.8300, -0.8127]],
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([[ 0.7472, 0.6851, 1.2551],
[-0.0878, 0.4658, -1.6747],
[ 1.9960, 0.9871, 0.6570]], requires_grad=True), Parameter containing:
tensor([[1.6334]], requires_grad=True)]
Output from the convolution: tensor([[-3.7339, 1.2239, 2.4967, 3.1292, -0.9233, 2.5143, 3.8173, 1.9978],
[-3.0738, -0.0657, 3.3257, -0.8936, 1.0549, 3.6882, 3.8383, 3.2575],
[ 2.7553, -1.8727, 4.2299, -3.1991, 4.4124, 6.5621, 5.3431, 1.0906],
[-2.6882, 3.4607, 2.5402, 3.6174, -2.6300, 2.3170, 11.4771, -1.5090],
[ 2.6341, -1.8004, 8.6334, 1.2461, 0.3524, 5.1714, 0.6487, 8.1190],
[-0.2742, -0.3234, -0.6728, 5.2681, 1.9085, 2.5781, 3.4493, 3.2762],
[10.4822, -1.2788, 1.8316, 2.3852, 5.7619, -2.0992, 1.7370, -0.2056],
[-1.1415, 0.8566, 1.5280, 3.7496, 0.2819, 0.9107, 1.7040, 1.6433]],
grad_fn=<ScipyConv2dFunctionBackward>)
Gradient for the input map: tensor([[ 0.2121, -0.9944, -0.1915, -1.6754, 0.0653, -0.3707, -0.0527, 1.9510,
1.4351, 0.8096],
[ 0.2721, 1.3443, -0.3287, 4.7906, -0.6643, 1.3229, 1.0289, -3.2899,
-4.1849, -2.5836],
[ 1.5136, -1.6320, 1.4152, -3.7734, -1.5929, -2.9689, 1.0763, 0.6364,
4.0895, 2.6705],
[ 0.4238, 3.5837, -0.5975, 2.3581, 1.5612, 2.4618, -5.0229, 0.6077,
-0.7515, -0.5809],
[ 3.2190, 1.7868, 0.6420, -5.9825, -4.3110, -2.2599, 3.0381, 0.9544,
2.1632, -0.9590],
[-0.0882, 3.1870, 3.9434, 2.1550, 1.0215, -0.0373, 4.2709, 5.3345,
-3.0363, 2.2078],
[ 2.2168, 0.8335, -2.2195, -2.2792, -6.8907, -0.1850, 7.9976, 1.3162,
-0.3433, -3.5967],
[ 2.5724, 3.6775, 4.6798, 1.8059, -1.2252, 1.9183, -0.5932, -0.4726,
2.1310, 1.7577],
[ 1.9800, 2.7016, -1.0075, -0.8502, 1.8089, -1.9287, -0.2462, -1.1557,
-2.4364, 0.5321],
[ 2.4236, 0.9034, -0.7870, 2.9086, -0.2799, -1.4548, -1.2136, -2.0594,
-0.6937, -0.5165]])
</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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