mirror of
https://github.com/cdtools-developers/cdtools.git
synced 2026-09-10 05:22:41 +02:00
54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
import cdtools
|
|
import torch as t
|
|
from matplotlib import pyplot as plt
|
|
|
|
filename = 'example_data/lab_ptycho_data.cxi'
|
|
dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(filename)
|
|
|
|
# FancyPtycho is the workhorse model
|
|
model = cdtools.models.FancyPtycho.from_dataset(
|
|
dataset,
|
|
n_modes=3, # Use 3 incoherently mixing probe modes
|
|
oversampling=2, # Simulate the probe on a 2xlarger real-space array
|
|
probe_support_radius=120, # Force the probe to 0 outside a radius of 120 pix
|
|
propagation_distance=5e-3, # Propagate the initial probe guess by 5 mm
|
|
units='mm', # Set the units for the live plots
|
|
obj_view_crop=-50, # Expands the field of view in the object plot by 50 pix
|
|
)
|
|
|
|
if t.cuda.is_available():
|
|
model.to(device='cuda')
|
|
dataset.get_as(device='cuda')
|
|
|
|
# For this script, we use a slightly different pattern where we explicitly
|
|
# create a `Reconstructor` class to orchestrate the reconstruction. The
|
|
# reconstructor will store the model and dataset and create an appropriate
|
|
# optimizer. This allows the optimizer to persist between loops, along with
|
|
# e.g. estimates of the moments of individual parameters
|
|
recon = cdtools.reconstructors.AdamReconstructor(model, dataset)
|
|
|
|
# The learning rate parameter sets the alpha for Adam.
|
|
# The beta parameters are (0.9, 0.999) by default
|
|
# The batch size sets the minibatch size
|
|
for loss in recon.optimize(50, lr=0.02, batch_size=10):
|
|
print(model.report())
|
|
# Because plotting can be expensive, setting a minimum plotting interval
|
|
# (in seconds) can avoid excessive replots.
|
|
model.inspect(dataset, min_interval=10)
|
|
|
|
# It's common to chain several different reconstruction loops. Here, we
|
|
# started with an aggressive refinement to find the probe in the previous
|
|
# loop, and now we polish the reconstruction with a lower learning rate
|
|
# and larger minibatch
|
|
for loss in recon.optimize(50, lr=0.005, batch_size=50):
|
|
print(model.report())
|
|
model.inspect(dataset, min_interval=10)
|
|
|
|
# This orthogonalizes the recovered probe modes
|
|
model.tidy_probes()
|
|
|
|
# Setting replot_all will reopen any windows which were closed earlier
|
|
model.inspect(dataset, replot_all=True)
|
|
model.compare(dataset)
|
|
plt.show()
|