from __future__ import division, print_function, absolute_import import torch as t from CDTools.models import CDIModel from CDTools import tools from CDTools.tools import plotting as p from copy import copy from torch.utils import data as torchdata from matplotlib import pyplot as plt class SimplePtycho(CDIModel): def __init__(self, wavelength, detector_geometry, probe_basis, detector_slice, probe_guess, obj_guess, min_translation = [0,0], mask=None): super(SimplePtycho,self).__init__() self.wavelength = t.Tensor([wavelength]) self.detector_geometry = copy(detector_geometry) det_geo = self.detector_geometry if hasattr(det_geo, 'distance'): det_geo['distance'] = t.Tensor(det_geo['distance']) if hasattr(det_geo, 'basis'): det_geo['basis'] = t.Tensor(det_geo['basis']) if hasattr(det_geo, 'corner'): det_geo['corner'] = t.Tensor(det_geo['corner']) self.min_translation = t.Tensor(min_translation) self.probe_basis = t.Tensor(probe_basis) self.detector_slice = detector_slice if mask is None: self.mask = None else: self.mask = t.ByteTensor(mask) # We rescale the probe here so it learns at the same rate as the # object self.probe_norm = t.max(tools.cmath.cabs(probe_guess.to(t.float32))) self.probe = t.nn.Parameter(probe_guess.to(t.float32) / self.probe_norm) self.obj = t.nn.Parameter(obj_guess.to(t.float32)) @classmethod def from_dataset(cls, dataset): wavelength = dataset.wavelength det_basis = dataset.detector_geometry['basis'] det_shape = dataset[0][1].shape distance = dataset.detector_geometry['distance'] # always do this on the cpu get_as_args = dataset.get_as_args dataset.get_as(device='cpu') (indices, translations), patterns = dataset[:] dataset.get_as(*get_as_args[0],**get_as_args[1]) center = tools.image_processing.centroid(t.sum(patterns,dim=0)) # Then, generate the probe geometry from the dataset ewg = tools.initializers.exit_wave_geometry probe_basis, probe_shape, det_slice = ewg(det_basis, det_shape, wavelength, distance, center=center) # Next generate the object geometry from the probe geometry and # the translations pix_translations = tools.interactions.translations_to_pixel(probe_basis, translations) obj_size, min_translation = tools.initializers.calc_object_setup(probe_shape, pix_translations) # Finally, initialize the probe and object using this information probe = tools.initializers.SHARP_style_probe(dataset, probe_shape, det_slice) obj = t.ones(obj_size+(2,)) det_geo = dataset.detector_geometry if hasattr(dataset, 'mask') and dataset.mask is not None: mask = dataset.mask.to(t.uint8) else: mask = None return cls(wavelength, det_geo, probe_basis, det_slice, probe, obj, min_translation=min_translation, mask=mask) def interaction(self, index, translations): pix_trans = tools.interactions.translations_to_pixel(self.probe_basis, translations) pix_trans -= self.min_translation return tools.interactions.ptycho_2D_round(self.probe_norm * self.probe, self.obj, pix_trans) def forward_propagator(self, wavefields): return tools.propagators.far_field(wavefields) def backward_propagator(self, wavefields): return tools.propagators.inverse_far_field(wavefields) def measurement(self, wavefields): return tools.measurements.intensity(wavefields, detector_slice=self.detector_slice) def loss(self, sim_data, real_data, mask=None): return tools.losses.amplitude_mse(real_data, sim_data, mask=mask) def to(self, *args, **kwargs): super(SimplePtycho, self).to(*args, **kwargs) self.wavelength = self.wavelength.to(*args,**kwargs) # move the detector geometry too det_geo = self.detector_geometry if hasattr(det_geo, 'distance'): det_geo['distance'] = det_geo['distance'].to(*args,**kwargs) if hasattr(det_geo, 'basis'): det_geo['basis'] = det_geo['basis'].to(*args,**kwargs) if hasattr(det_geo, 'corner'): det_geo['corner'] = det_geo['corner'].to(*args,**kwargs) if self.mask is not None: self.mask = self.mask.to(*args, **kwargs) self.min_translation = self.min_translation.to(*args,**kwargs) self.probe_basis = self.probe_basis.to(*args,**kwargs) self.probe_norm = self.probe_norm.to(*args,**kwargs) def sim_to_dataset(self, args_list): raise NotImplementedError() def inspect(self): p.plot_amplitude(self.probe, basis=self.probe_basis) plt.title('Probe Amplitude') p.plot_phase(self.probe, basis=self.probe_basis) plt.title('Probe Phase') p.plot_amplitude(self.obj, basis=self.probe_basis) plt.title('Object Amplitude') p.plot_phase(self.obj, basis=self.probe_basis) plt.title('Object Phase') def save_results(self): probe = tools.cmath.torch_to_complex(self.probe.detach().cpu()) probe = probe * self.probe_norm.detach().cpu().numpy() obj = tools.cmath.torch_to_complex(self.obj.detach().cpu()) return {'probe':probe,'obj':obj} def ePIE(self, iterations, dataset, beta = 1.0): """Runs an ePIE reconstruction as described in `Maiden et al. (2017) `_. Optional parameters are: :arg ``iterations``: Controls the number of iterations run, defaults to 1. :arg ``beta``: Algorithmic parameter described in Maiden's implementation of rPIE. Defaults to 0.15. :arg ``probe``: Initial probe wavefunction. :arg ``object``: Initial object wavefunction. """ probe_shape = self.probe.shape if self.mask is not None: mask = self.mask[...,None] else: mask=None def probe_update(exit_wave, exit_wave_corrected, probe, object, translation): return probe+tools.cmath.cmult(beta*(tools.cmath.cconj(object)/t.max(tools.cmath.cabssq(object)))[translation[0]:translation[0]+probe_shape[0],translation[1]:translation[1]+probe_shape[1]], \ exit_wave_corrected-exit_wave) def object_update(exit_wave, exit_wave_corrected, probe, object, translation): object[translation[0]:translation[0]+probe_shape[0],translation[1]:translation[1]+probe_shape[1]]\ +=tools.cmath.cmult(tools.cmath.cconj(probe)/t.max(tools.cmath.cabssq(probe)),exit_wave_corrected-exit_wave) return object with t.no_grad(): data_loader = torchdata.DataLoader(dataset, shuffle=True) for it in range(iterations): loss = [] for (i, [translations]), [patterns] in data_loader: probe = self.probe.clone() object = self.obj.clone() exit_wave = self.interaction(i, translations) exit_wave_corrected = exit_wave.clone() exit_wave_corrected[self.detector_slice] = tools.projectors.modulus(self.forward_propagator(exit_wave)[self.detector_slice], patterns, mask = mask) integer_translations = t.round(translations).to(dtype=t.int32) self.probe.data = probe_update(exit_wave, exit_wave_corrected, probe, object, integer_translations) self.obj.data = object_update(exit_wave, exit_wave_corrected, probe, object, integer_translations) loss.append(self.loss(self.measurement(self.interaction(i, translations)), patterns)) yield t.mean(t.Tensor(loss)).cpu().numpy()