Skip to content

Latest commit

 

History

History
83 lines (53 loc) · 3.56 KB

visualization_guide.md

File metadata and controls

83 lines (53 loc) · 3.56 KB
Raw

Visualize learned features

Intructions

In order to visualize features you need a dataset and a pretrained model. You can use one of our pretrained models provided in the pretrained models guide, and the corresponding dataset.

To start this visualization run the script:

    python3 visualize_features.py

Details

The visualization script has to main parts, separated in two different methods of the visualizer class in visualizer.py.

  • In the first part, implemented in the method top_relu_activations, the script runs the model on test examples (forward pass). At the chosen Relu layer, you have N output features that are going to be visualized. For each feature, the script keeps the top 5 examples that activated it the most, and saves them in a visu folder.

  • In the second part, implemented in the method top_relu_activations, the script just shows the saved examples for each feature with the level of activation as color. You can navigate through examples with keys 'g' and 'h'.

N.B. This second part of the code can be started without doing the first part again if the top examples have already been computed. See details in the code. Alternatively you can visualize the saved example with a point cloud software like CloudCompare.

Visualize kernel deformations

Intructions

In order to visualize features you need a dataset and a pretrained model that uses deformable KPConv. You can use our NPM3D pretrained model provided in the pretrained models guide.

To start this visualization run the script:

    python3 visualize_deformations.py

Details

The visualization script runs the model runs the model on a batch of test examples (forward pass), and then show these examples in an interactive window. Here is a list of all keyborad shortcuts:

  • 'b' / 'n': smaller or larger point size.
  • 'g' / 'h': previous or next example in current batch.
  • 'k': switch between the rigid kenrel (original kernel points positions) and the deformed kernel (position of the kernel points after shift are applied)
  • 'z': Switch between the points displayed (input points, current layer points or both).
  • '0': Saves the example and deformed kernel as ply files.
  • mouse left click: select a point and show kernel at its location.
  • exit window: compute next batch.

visualize Effective Receptive Fields

Intructions

In order to visualize features you need a dataset and a pretrained model. You can use one of our pretrained models provided in the pretrained models guide, and the corresponding dataset.

To start this visualization run the script:

    python3 visualize_ERFs.py

Warning: This cript currently only works on the following datasets: NPM3D, Semantic3D, S3DIS, Scannet

Details

The visualization script show the Effective receptive fields of a network layer at one location. If you chose another location (with left click), it has to rerun the model on the whole input point cloud to get new gradient values. Here a list of all keyborad shortcuts:

  • 'b' / 'n': smaller or larger point size.
  • 'g' / 'h': lower or higher ceiling limit. A functionality that remove points from the ceiling. Very handy for indoor point clouds.
  • 'z': Switch between the points displayed (input points, current layer points or both).
  • 'x': Go to the next input point cloud.
  • '0': Saves the input point cloud with ERF values and the center point used as origin of the ERF.
  • mouse left click: select a point and show ERF at its location.
  • exit window: End script.