InShaPe dataset: Large-scale multi-shape phase retrieval dataset for PBF-LB/M laser beam shaping SLM phase mask estimation.

This simulated InShaPe dataset containing six beam shapes is used for training and testing on the complex forward path application, PBF-LB/M beam shaping system, in our paper "Efficient Gerchberg-Saxton algorithm deep unrolling for phase retrieval with complex forward path". In this dataset, there are 6 sub-datasets stored in 6 zip folders named by "dense+[beamshape name]+30k_pre.zip". For each beam shape, the trainging set has 10000 pairs of aberrated PBF-LB/M beam shape samples. Each paired sample contains one intensity profile of the aberrated beam shape in an imaging plane 5 times Rayleigh length before the back focal plane and one corresponding aberrated SLM phase mask that generated the aberrated beam shape. The aberrations in the paired samples are modelled with linear combination of Zernike polynomials amplified by randomly sampled Zernike coefficients.In order for the convenience of distributing the dataset, this repository does not contain the simulated paired samples. Instead, we give the original Zernike coefficients stored in Numpy array .npy files and the corresponding simulation script so that the users can run the simulation to reproduce the entire dataset. The Zernike coefficients .npy files generating the training set and test set reside in the subfolders "Output_Data" and "Val_Data", respectively, in each beam shape's zip folder. The simulation scripts generating the training set and test set reside in the root directory of each beam shape's zip folder named "data_processing_train.py" and "data_processing_test.py", respectively. Run the simulation scripts then the paired samples for training and testing will be generated in the subfolders "training_set" and "test_sample" in the zip folder of each beam shape. The generated intensity profiles and phase masks are also in numpy .npy files.