Buy new ram error

Ferruolo: <p>Hello everyone,</p>
<p>I’m trying to build YOLO on pytorch, both for Academic reasons(I’m rather new to deep learning) and because I’m trying to use it to control a robot. However when I run the following line to create a new layer</p>
<pre><code class="lang-auto">Self.conv10 = nn.Conv3d(9,4,1024)
</code></pre>
<p>Which is one of the layers detailed in the paper, I receive an error telling me that I allocated 114gb of ram and that ends with Buy New Ram! If you need to know my computer specs, I have a SurfacePro with 16gb ram, and I8, and a NVIDIA 1069</p>

Mazhar_Shaikh: <p>Hi Ferruolo,<br>
The conv3d parameters are input channels, output channels and kernel size in that order. Please ensure this is consistent with your code.<br>
As you have given kernel size as 1024, the resulting weight tensor is of the size 9 x 4x 1024 x 1024 x 1024. That is a highly unusual kernel size in current literature.</p>

Ferruolo: <p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/2X/a/a28059cacb043a058ed8e00532b7efe7c24801e0.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/a28059cacb043a058ed8e00532b7efe7c24801e0" title="image"><img src="https://discuss.pytorch.org/uploads/default/optimized/2X/a/a28059cacb043a058ed8e00532b7efe7c24801e0_2_690x327.jpeg" alt="image" data-base62-sha1="nbyl6BsoPfJoFmn5Y2A7xwV24LK" width="690" height="327" srcset="https://discuss.pytorch.org/uploads/default/optimized/2X/a/a28059cacb043a058ed8e00532b7efe7c24801e0_2_690x327.jpeg, https://discuss.pytorch.org/uploads/default/original/2X/a/a28059cacb043a058ed8e00532b7efe7c24801e0.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/original/2X/a/a28059cacb043a058ed8e00532b7efe7c24801e0.jpeg 2x" data-dominant-color="F6F6F6"><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">828×393 54.6 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>I pulled that diagram from the Yolo paper. As far as I am aware, the notation of the kernels is INxOUTxKERNEL_SIZE. However, I am self taught, so that may be where I am wrong. What would I want to use for the 3x3x1024_kernel layer then?</p>

Gullal: <p><a class="mention" href="/u/ferruolo">@Ferruolo</a> - You should be using conv2d here instead of conv3d.<br>
For 3x3x1024, it means that you have 1024 kernel filters of 3x3 size each. So your out_channels here is 1024, kernel_size is 3x3 and in_channels is the number of channels from the previous layer.</p>

Ferruolo: <p>Thanks a lot for your help <a class="mention" href="/u/gullal">@Gullal</a> and <a class="mention" href="/u/mazhar_shaikh">@Mazhar_Shaikh</a>.</p>

Gullal: <p><a class="mention" href="/u/ferruolo">@Ferruolo</a> - Pytorch Docs are very detailed and easy to follow, and there are plenty of tutorials on github. You can follow them to learn more.</p>

Nikronic: <p>I think every time someone opens PyTorch forum, at first should be redirected to this phrase.</p>