add Topological Optimization code #1043
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Hi Mohamed, At this point, the PR doesn't pass the continuous integration (CI), which stopped on a third-party dependency issue (here, torch). As you will read in this documentation (https://github.com/topology-tool-kit/ttk/wiki/Guidelines-for-Developing-a-New-TTK-Module), TTK must build and run even when third-party dependencies haven't been found. This means that your code should build and run, even if torch is not installed. In particular, if the optimization backend selected by the user was Adam and that Torch was not installed, you should display a warning ( To know in your C++ code if Torch has been found or not, use the preprocessor instruction Please proceed to this change and fix any issue until the CI succeeds. |
…opologicalOptimization
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| - neighborsIndices : vector which contains the neighboring vertices of | ||
| vertex i | ||
| */ | ||
| template <typename triangulationType> |
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Please remove that function (see my comments below when it is used).
| needUpdate[indice] = true; | ||
| // Find all the neighbors of the vertex | ||
| std::vector<SimplexId> neighborsIndices; | ||
| getNeighborsIndices(triangulation, indice, neighborsIndices); |
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please remove that call.
| std::vector<SimplexId> neighborsIndices; | ||
| getNeighborsIndices(triangulation, indice, neighborsIndices); | ||
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| for(SimplexId neighborsIndice : neighborsIndices) { |
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replace that loop by:
int vertexNumber = triangulation->getVertexNeighborNumber(index);
for(int i = 0; i < vertexNumber; i++){
int vertexNeighborId = -1;
triangulation->getVertexNeighbor(index, i, vertexNeighborId);
needUpdate[vertexNeighborId] = true;
}
| vertex i | ||
| */ | ||
| template <typename triangulationType> | ||
| int ttk::TopologicalOptimization::getNeighborsIndices( |
Hi Julien, Thank you for your feedback. It's duly noted; I have removed the getNeighborsIndices function and changed the type of the dataVector variable to dataType. |
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Hi Mohamed, thanks a lot for your edits.
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Moreover, the input data must be first normalized in a pre-process, and "de-normalized" in a post-process. |
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Also, please report a status message every ten iterations (and not every iterations). Thanks! |
…y between kFloat64 and kDouble, remove unnecessary copies of smoothedScalars. Normalize input data in pre-process and de-normalize in post-process. Report status message every k iterations.
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hi mohamed, it seems that your last commit is breaking things though. please consider the statefile I sent you in slack, in the TopologicalSimplification node, switch from LTS to TopologicalOptimization and you'll see that the direct gradient descent method generates incorrect results (the adam method looks OK). |
…f the target diagram.
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OK, looks good to me. Thanks a lot! |
Integration of the code for topological simplification based on persistence diagram optimization.