Expected 4-dimensional input for 4-dimensional weight [64, 1, 4, 4], but got 3-dimensional input of size [3, 256, 256] instead

Hillary: <p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/e/8/e8dcee13f92b6d1d0f5546df9089c4c3f54eae7b.png" data-download-href="https://discuss.pytorch.org/uploads/default/e8dcee13f92b6d1d0f5546df9089c4c3f54eae7b" title="image"><img src="https://discuss.pytorch.org/uploads/default/original/3X/e/8/e8dcee13f92b6d1d0f5546df9089c4c3f54eae7b.png" alt="image" data-base62-sha1="xe072aYQez5jMhcAZigCbSDT5jl" width="403" height="499" data-dominant-color="EEEEEE"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">698×866 41.4 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
I got this error from the above code, Kindly assist :Expected 4-dimensional input for 4-dimensional weight [64, 1, 4, 4], but got 3-dimensional input of size [3, 256, 256] instead</p>

thecho7: <p>Your input seems to have 3-dimensions.<br>
If you’re using <code>nn.Conv2d</code>, the input should have 4-dimensions.</p>
<p>Try do it</p>
<pre><code class="lang-auto">data = data.unsqueeze(0)
</code></pre>
<p>Before the model inference</p>

Hillary: <aside class="quote no-group" data-username="thecho7" data-post="2" data-topic="157246">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/eada6e/48.png" class="avatar"> thecho7:</div>
<blockquote>
<p><code>data = data.unsqueeze(0)</code></p>
</blockquote>
</aside>
<p>Thanks for assistance, After adding the above line,  it gave me the following error<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/5/6/56016f4a8a1210f9b59086e3adcacbadbaa643fb.png" data-download-href="https://discuss.pytorch.org/uploads/default/56016f4a8a1210f9b59086e3adcacbadbaa643fb" title="image"><img src="https://discuss.pytorch.org/uploads/default/original/3X/5/6/56016f4a8a1210f9b59086e3adcacbadbaa643fb.png" alt="image" data-base62-sha1="cgQ8s9qpP77amEgmUpCs78mTYf1" width="344" height="500" data-dominant-color="ECEEEE"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">568×825 38 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>
<p>RuntimeError: Given groups=1, weight of size [64, 1, 4, 4], expected input[1, 3, 256, 256] to have 1 channels, but got 3 channels instead</p>

thecho7: <p>The network is designed to train a gray scale image which has 1-channel while a RGB image has 3-channels.</p>
<p>In short, change the number of channels of the first convolution by</p>
<pre><code class="lang-auto">nc=3
</code></pre>

Hillary: <p>Thanks a lot, it solved the issue</p>

thecho7: <p>Please check the reply as a solution to let others know this is solved <img src="https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=12" title=":slight_smile:" class="emoji" alt=":slight_smile:" loading="lazy" width="20" height="20"></p>