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 cmath import numpy as np from copy import copy class FancyPtycho(CDIModel): def __init__(self, wavelength, detector_geometry, probe_basis, detector_slice, probe_guess, obj_guess, min_translation = t.Tensor([0,0]), background = None, translation_offsets=None, mask=None, weights = None, translation_scale = 1, saturation=None, probe_support = None): super(FancyPtycho,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 self.saturation = saturation if mask is None: self.mask = mask else: self.mask = t.ByteTensor(mask) # We rescale the probe here so it learns at the same rate as the # object if probe_guess.dim() > 3: self.probe_norm = t.max(tools.cmath.cabs(probe_guess[0].to(t.float32))) else: 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)) if background is None: background = 1e-6 * t.ones(self.probe[0][self.detector_slice].shape[:-1]) self.background = t.nn.Parameter(t.Tensor(background).to(t.float32)) if weights is None: self.weights = None else: self.weights = t.nn.Parameter(t.Tensor(weights).to(t.float32)) if translation_offsets is None: self.translation_offsets = None else: self.translation_offsets = t.nn.Parameter(t.Tensor(translation_offsets).to(t.float32)/ translation_scale) self.translation_scale = translation_scale if probe_support is not None: self.probe_support = probe_support else: self.probe_support = t.ones_like(self.probe[0]) @classmethod def from_dataset(cls, dataset, probe_size=None, randomize_ang=0, padding=0, n_modes=1, translation_scale = 1, saturation=None, probe_support_radius=None): 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]) # Set to none to avoid issues with things outside the detector 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, padding=padding, opt_for_fft=False) # 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, padding=50) # Finally, initialize the probe and object using this information if probe_size is None: probe = tools.initializers.SHARP_style_probe(dataset, probe_shape, det_slice) else: probe = tools.initializers.gaussian_probe(dataset, probe_basis, probe_shape, probe_size) # Now we initialize all the subdominant probe modes probe_max = t.max(cmath.cabs(probe)) probe_stack = [0.01 * probe_max * t.rand(probe.shape,dtype=probe.dtype) for i in range(n_modes - 1)] probe = t.stack([probe,] + probe_stack) obj = tools.cmath.expi(randomize_ang * (t.rand(obj_size)-0.5)) det_geo = dataset.detector_geometry translation_offsets = 0 * (t.rand((len(dataset),2)) - 0.5) weights = t.ones(len(dataset)) if hasattr(dataset, 'mask') and dataset.mask is not None: mask = dataset.mask.to(t.uint8) else: mask = None if probe_support_radius is not None: probe_support = t.zeros_like(probe[0].to(dtype=t.float32)) p_cent = np.array(probe.shape[1:3]).astype(int) // 2 psr = int(probe_support_radius) probe_support[p_cent[0]-psr:p_cent[0]+psr, p_cent[1]-psr:p_cent[1]+psr] = 1 else: probe_support = t.ones_like(probe[0].to(dtype=t.float32)) #return cls(wavelength, det_geo, probe_basis, det_slice, probe, obj, min_translation=min_translation, translation_offsets = translation_offsets) return cls(wavelength, det_geo, probe_basis, det_slice, probe, obj, min_translation=min_translation, translation_offsets = translation_offsets, weights=weights, mask=mask, translation_scale=translation_scale, saturation=saturation, probe_support=probe_support) def interaction(self, index, translations): pix_trans = tools.interactions.translations_to_pixel(self.probe_basis, translations) pix_trans -= self.min_translation if self.translation_offsets is not None: pix_trans += self.translation_scale * self.translation_offsets[index] all_exit_waves = [] for i in range(self.probe.shape[0]): pr = self.probe[i] * self.probe_support #exit_waves = self.probe_norm * tools.interactions.ptycho_2D_round(self.probe[i], # self.obj, # pix_trans) exit_waves = self.probe_norm * tools.interactions.ptycho_2D_sinc(pr, self.obj, pix_trans, shift_probe=True) exit_waves = exit_waves * self.probe_support[...,:,:] if exit_waves.dim() == 4: exit_waves = self.weights[index][:,None,None,None] * exit_waves else: exit_waves = self.weights[index] * exit_waves all_exit_waves.append(exit_waves) return t.stack(all_exit_waves) 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.quadratic_background(wavefields, self.background, detector_slice=self.detector_slice, measurement=tools.measurements.incoherent_sum, saturation=self.saturation ) 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(FancyPtycho, 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) self.probe_support = self.probe_support.to(*args,**kwargs) def sim_to_dataset(self, args_list): pass