@@ -116,6 +116,8 @@ You need to configure a few important paths that are used in the code to read in
# Paths to preexisting databases
# Folder containing Multi-PIE images
bob config set bob.db.multipie.directory <path_to_folder> (*data* folder in the downloaded database).
# Folder containing FFHQ images
bob config set bob.db.ffhq.directory <path_to_folder>
# Paths to generated content
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@@ -125,6 +127,8 @@ You need to configure a few important paths that are used in the code to read in
bob config set bob.synface.latent_directions <path_to_pickle_file.pkl>
# Folder to store generated data
bob config set bob.synface.synthetic_datasets <path_to_folder>
# Folder to score biometric evaluation results
bob config set bob.synface.scores <path_to_folder>
Run dataset projection
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@@ -154,7 +158,7 @@ in the latent space:
./bin/latent_analysis.py --seed 0
The results will be stored in the Pickle file pointed by the `bob.synface.latent_directions` entry of the `.bobrc` configuration file.
In case you don't want to regenerate yourself those latent directions, we provide them already in this repo under the path `precomputed/latent_directions.pkl`.
Generate a synthetic database
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@@ -187,9 +191,15 @@ the identities are split to generate all variations.
Run benchmark experiments
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Upcoming
Contrary to all previous scripts, the benchmark experiments are based on Bob 9.0 and Tensorflow 2. Therefore, one rerun the :code:`buildout` command with the Bob 9.0 environment as a basis.
This is done with:
::
conda activate synbenchmark
buildout
which should in particular create
1. `./bin/python` Custom Python executable containing the benchmark env. extended with `bob.paper.ijcb2021_synthetic_dataset`
2. `./bin/uniqueness_experiment.py` Custom benchmark script to compare identities from two different database (Seed and Synthetic).
