83 lines
3.6 KiB
Markdown
83 lines
3.6 KiB
Markdown
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## Visualize learned features
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### Intructions
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In order to visualize features you need a dataset and a pretrained model. You can use one of our pretrained models
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provided in the [pretrained models guide](./pretrained_models_guide.md), and the corresponding dataset.
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To start this visualization run the script:
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python3 visualize_features.py
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### Details
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The visualization script has to main parts, separated in two different methods of the visualizer class in
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`visualizer.py`.
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* In the first part, implemented in the method `top_relu_activations`, the script runs the model on test examples
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(forward pass). At the chosen Relu layer, you have N output features that are going to be visualized. For each feature,
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the script keeps the top 5 examples that activated it the most, and saves them in a `visu` folder.
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* In the second part, implemented in the method `top_relu_activations`, the script just shows the saved examples for
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each feature with the level of activation as color. You can navigate through examples with keys 'g' and 'h'.
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N.B. This second part of the code can be started without doing the first part again if the top examples have already
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been computed. See details in the code. Alternatively you can visualize the saved example with a point cloud software
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like CloudCompare.
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## Visualize kernel deformations
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### Intructions
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In order to visualize features you need a dataset and a pretrained model that uses deformable KPConv. You can use our
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NPM3D pretrained model provided in the [pretrained models guide](./pretrained_models_guide.md).
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To start this visualization run the script:
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python3 visualize_deformations.py
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### Details
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The visualization script runs the model runs the model on a batch of test examples (forward pass), and then show these
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examples in an interactive window. Here is a list of all keyborad shortcuts:
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- 'b' / 'n': smaller or larger point size.
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- 'g' / 'h': previous or next example in current batch.
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- 'k': switch between the rigid kenrel (original kernel points positions) and the deformed kernel (position of the
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kernel points after shift are applied)
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- 'z': Switch between the points displayed (input points, current layer points or both).
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- '0': Saves the example and deformed kernel as ply files.
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- mouse left click: select a point and show kernel at its location.
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- exit window: compute next batch.
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## visualize Effective Receptive Fields
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### Intructions
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In order to visualize features you need a dataset and a pretrained model. You can use one of our pretrained models
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provided in the [pretrained models guide](./pretrained_models_guide.md), and the corresponding dataset.
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To start this visualization run the script:
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python3 visualize_ERFs.py
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**Warning: This cript currently only works on the following datasets: NPM3D, Semantic3D, S3DIS, Scannet**
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### Details
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The visualization script show the Effective receptive fields of a network layer at one location. If you chose another
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location (with left click), it has to rerun the model on the whole input point cloud to get new gradient values. Here a
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list of all keyborad shortcuts:
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- 'b' / 'n': smaller or larger point size.
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- 'g' / 'h': lower or higher ceiling limit. A functionality that remove points from the ceiling. Very handy for indoor
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point clouds.
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- 'z': Switch between the points displayed (input points, current layer points or both).
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- 'x': Go to the next input point cloud.
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- '0': Saves the input point cloud with ERF values and the center point used as origin of the ERF.
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- mouse left click: select a point and show ERF at its location.
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- exit window: End script.
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