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49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
from __future__ import division, print_function, absolute_import
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import CDTools
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from matplotlib import pyplot as plt
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import pickle
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from torch.utils.data import Subset
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# First, we load an example dataset from a .cxi file
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ss_filename = 'example_data/Optical_Data_ss.cxi'
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with open('example_data/Optical_ptycho.pickle', 'rb') as f:
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ptycho_results = pickle.load(f)
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probe = ptycho_results['probe']
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background = ptycho_results['background']
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dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(ss_filename)
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# Next, we create a ptychography model from the dataset
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# Note that we explicitly as for two incoherent probe modes
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model = CDTools.models.RPI.from_dataset(dataset, probe, [800,800],
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background=background, n_modes=2)
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# Let's do this reconstruction on the GPU, shall we?
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model.to(device='cuda')
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dataset.get_as(device='cuda')
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# Note that the inspect step takes the vast majority of the time
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# The regularization is an L2 regularizer that empirically helps accelerate
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# convergence
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for i, loss in enumerate(model.LBFGS_optimize(30, dataset, lr=0.4, regularization_factor=[0.05,0.05])):#0.1)):
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model.inspect(dataset)
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print(i,loss)
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# Now we use the regularizer to damp all but the top modes
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for i, loss in enumerate(model.LBFGS_optimize(20, dataset, lr=0.4, regularization_factor=[0.001,0.1])):
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model.inspect(dataset)
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print(i,loss)
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results = model.save_results()
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# Finally, we plot the results
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model.inspect(dataset)
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model.compare(dataset)
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plt.show()
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