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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>
<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>
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<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></li>
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๊ฐ๋ฐํ๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="rpc_ddp_tutorial.html">Combining Distributed DataParallel with Distributed RPC Framework</a></li>
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<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</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([[ 1.2797, 0.2314, 4.3470, 0.4024, 1.3932],
[ 2.4791, 6.2748, 6.9746, 4.4018, 2.9865],
[ 6.6573, 5.0463, 3.5395, 12.0947, 1.2748],
[ 7.4198, 2.0305, 5.5780, 6.8367, 2.0566],
[ 1.3851, 6.7892, 3.1325, 6.2062, 3.4733],
[ 7.4198, 5.7227, 2.7209, 1.7376, 2.0566],
[ 6.6573, 7.6933, 2.2952, 4.1262, 1.2748],
[ 2.4791, 6.4630, 5.9324, 14.4268, 2.9865]],
grad_fn=<BadFFTFunctionBackward>)
tensor([[-0.1562, 0.8767, -0.0279, -0.5590, -0.4049, -0.6294, 0.0552, 0.4810],
[ 0.1601, -1.1035, 0.3513, 0.1299, -1.6969, 1.3007, -0.4422, -1.1647],
[ 0.9429, -0.4646, 0.6966, -0.4873, -0.5495, 0.6050, -0.6683, 0.7096],
[-0.2174, 0.1388, -0.1296, 0.5177, -0.5455, -0.4387, 0.9784, 0.0586],
[ 0.9471, 0.9292, -1.0623, -0.2889, 0.7687, -1.0495, 1.0651, 0.3339],
[-0.3611, -0.3248, -0.3803, 1.0753, 0.6996, -0.4564, -0.4181, -0.0440],
[-1.3854, -0.8065, 0.0863, 0.7329, 0.5444, -1.1850, -0.9978, -0.3841],
[-0.3915, -0.2883, 0.2269, -0.0591, 0.9739, 0.7514, 0.0020, 1.1501]],
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([[-2.5243, -1.0150, -1.5521],
[-1.1276, -0.2158, 2.1945],
[-0.5092, 1.3735, 0.7683]], requires_grad=True), Parameter containing:
tensor([[1.0292]], requires_grad=True)]
Output from the convolution: tensor([[12.0729, 11.2799, -1.3508, 7.9666, 2.4071, -2.5728, 1.2631, -2.2254],
[ 5.1248, -5.9427, 0.3868, -6.8096, 5.0938, -0.2407, -3.7427, 2.8156],
[ 0.3711, -7.5121, -7.4052, 5.2217, -3.3303, -0.9389, -3.7406, -1.4082],
[ 0.7297, -7.4242, 0.3990, -0.1360, 2.2693, 3.8970, 2.4131, 5.6190],
[ 4.7854, 1.0052, 0.9780, 2.0223, -4.0394, -0.8323, -2.5231, 3.4788],
[ 6.7039, -1.0170, 1.5981, -1.0614, -5.2726, -1.0443, 0.0517, 2.3737],
[ 3.0579, 1.7044, -4.6158, 1.0891, -7.5621, 1.0804, -0.1395, 4.5199],
[ 6.9795, 5.1657, -0.2977, 1.3772, -0.1371, 8.5715, 8.0620, -1.1346]],
grad_fn=<ScipyConv2dFunctionBackward>)
Gradient for the input map: tensor([[ 2.4108, 2.6134, 3.3406, 0.5703, 6.2952, 3.6062, 9.3566,
4.2264, 3.5191, 0.6884],
[ -1.7229, 4.3256, -1.8665, -0.7058, -1.8942, 2.5292, -3.8888,
-2.4863, -5.8173, -2.8729],
[ -5.2627, 0.8230, 2.7698, -2.2451, 0.6058, -3.0391, -1.6006,
-11.8203, -3.2296, -0.2084],
[ 0.7509, 3.0000, 6.2576, -2.8954, -3.7470, 1.2655, 0.1157,
-4.4041, -1.7423, 2.2945],
[ 0.2037, 3.3253, -2.2032, -2.8957, -3.9915, -0.5601, 0.9756,
-5.8087, 2.7004, 3.6449],
[ -1.1448, -4.2884, -2.4759, -7.1964, -0.4191, -0.7683, -0.5153,
1.6459, 3.9410, 3.1264],
[ 0.3072, 0.9257, -0.7709, -1.5242, -4.0268, 0.5955, -1.0279,
-0.1742, 0.7766, -1.9778],
[ 1.7734, 2.8678, -4.2461, -3.6873, -4.5440, 2.9827, 5.0381,
0.8785, -3.0739, -0.2007],
[ 0.9431, 0.2556, -5.0421, -1.0005, 3.1460, 4.6598, 6.2201,
-5.9244, -0.7758, 2.5623],
[ 0.3239, -0.6457, -2.1525, 2.3177, 1.2696, 3.1728, -0.9972,
-2.4185, 0.8936, 0.6967]])
</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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