Checks / structure
Conv feeds Linear with no flatten / pool
Check R18. Runs in the editor as you build, in CI through the
GitHub Action, and over the wire at
POST /api/v1/check. Milliseconds, before any GPU is billed.
block
structure
R18
| Trigger | A convolution (conv1d/2d/3d or a depthwise/separable/transpose variant) connects directly into a linear layer. |
|---|---|
| Why | Conv outputs a multi-dimensional feature map; Linear expects a flat [batch, features] tensor. The forward pass raises a shape error, or silently mis-multiplies the spatial dims. Insert a Flatten or a Global Average Pool between them. |
| Source | Standard CNN classifier construction (e.g. LeNet / AlexNet head). |
Why it is not a lint you can ignore
A structural mistake does not fail at review time and it does not fail at import
time. It fails when the module is constructed on the training node, after the job was queued and
the dataset was downloaded. That is why this runs before the spend and not after it.
See this check fire on a planted example
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Every check
41 structural checks: 6 guardrail gates and 35 architecture advisor rules. See the full catalogue.
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