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Task Relatedness Estimation with ModelGiF

Affinity Matrix Figure

Prepare data and models

Unzip the reference data(cutmix) to ./data.

Task relatedness estimation

Generate the gradient field for all taskonomy pretrained models with the specified reference data.

python generate_field.py --exp=1 -r=./result_featuremap_tk_rb -i=reference_data_cutmix_1000 -p=result_cutmix_1000 -g=0
  • -r The root directory where intermediate results are saved.
  • -i directory of the reference data
  • -p The file where the intermediate results are saved in the project's directory.
  • -g Select the gpu according to its id.
  • --exp Calculate the gradient according to what activation target. As the shape of output(NxCxHxW),
    • 1 Maximize the sum of the maximum values of all feature maps(HxW) in the last layer.
    • 5 Maximize the sum of the maximum values of all channels in the last layer.
    • 6 Maximize the total output of all neurons in the last layer.

Calculate the affinity matrix and verify that the functionality similarity obtained by ModelGiF positively correlates with that from taskonomy.

python compare_field.py -d=0 -r=./result_featuremap_tk_rb -p=result_cutmix_1000 -g=1
  • -d Distance measurement. 0 Cosine dissimilarity,1 Euclidean distance
  • -r The root directory where intermediate results are saved. Keep consistent with generate_field.py
  • -p The file where the intermediate results are saved in the project's directory，Keep consistent with generate_field.py
  • -g Select the gpu according to its id.

The results are in the last few lines of the .log file from compare_field.py.

  9.87795949e-01 9.96031106e-01 9.98350918e-01 2.95162206e-09
  9.90921795e-01]
 [9.97791469e-01 9.93405759e-01 9.96294141e-01 9.96240139e-01
  9.93965328e-01 9.97385919e-01 9.95509982e-01 9.93018031e-01
  9.89939630e-01 9.95451331e-01 9.92023647e-01 9.98503745e-01
  9.93933260e-01 9.96004224e-01 9.99510646e-01 9.90921795e-01
  3.08394421e-09]]
[-0.74509804 -0.85294118 -0.76470588 -0.89460784 -0.88480392 -0.90196078
 -0.86029412 -0.92647059 -0.95343137 -0.95588235 -0.81617647 -0.76960784
 -0.80637255 -0.86764706 -0.91666667 -0.71323529 -0.56372549]
./result_featuremap_tk_rb/result_cutmix_1000
Spearman correlation: -0.8349192618223761

Visualization

Visualize affinity matrix

python plot_heatmap.py
  • If you want to visualize the other affinity matrix, please modify the variable affi in the plot_heatmap.py manually.
  • The affinity matrix of ModelGiF can be obtained in the last few lines of the .log file from compare_field.py.

Affinity Matrix from ModelGIF

Similarity tree

python plot_tree.py
  • If you want to plot the similarity tree by other affinity matrix, please modify the variable affi in the plot_tree.py manually.
  • The affinity matrix of ModelGiF can be obtained in the last few lines of the .log file from compare_field.py.

Similarity Tree from ModelGIF