Evaluation module (+ code for training/eval FeatureFlow) #5
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…able_layers the ones from the pretrained feature encoder.
…e to visualize the qqplots and the reconstructed images from the base distributions.
giorgiapitteri
commented
Jun 20, 2024
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| def latent_radial_qqplot(models: Dict[str, Flow], data: datasets, n_samples, save_to=None): |
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This function is similar to the one here: https://github.com/aai-institute/USFlows/blob/main/src/explib/visualization.py. However it has been modified to work with the FeatureFlow (even if the reconstruction process is not working properly).
giorgiapitteri
commented
Jun 20, 2024
| plt.close() | ||
| return np.transpose(np_imgs, (1,2, 0)) | ||
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| def plot_digits(models: dict[str, Flow], n_samples=100, im_shape=(28, 28), save_to=None): #sqrtn: int |
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This function is similar to the one in: https://github.com/aai-institute/USFlows/blob/main/src/explib/visualization.py. However the original function calls the nsample function that I couldn't find anywhere.
giorgiapitteri
changed the title
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PR for two main features:
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