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420 lines
17 KiB
Python
420 lines
17 KiB
Python
from __future__ import division, print_function, absolute_import
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import torch as t
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from CDTools.models import CDIModel
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from CDTools.datasets import Ptycho2DDataset
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from CDTools import tools
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from CDTools.tools import cmath
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from CDTools.tools import plotting as p
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from matplotlib import pyplot as plt
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from datetime import datetime
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import numpy as np
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from copy import copy
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class Multislice2DPtycho(CDIModel):
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def __init__(self, wavelength, detector_geometry,
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probe_basis,
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probe_guess, obj_guess, dz, nz,
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detector_slice=None,
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surface_normal=np.array([0.,0.,1.]),
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min_translation = t.Tensor([0,0]),
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background = None, translation_offsets=None, mask=None,
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weights = None, translation_scale = 1, saturation=None,
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probe_support = None, obj_support=None, oversampling=1):
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super(Multislice2DPtycho,self).__init__()
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self.wavelength = t.Tensor([wavelength])
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self.detector_geometry = copy(detector_geometry)
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self.dz = dz
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self.nz = nz
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det_geo = self.detector_geometry
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if hasattr(det_geo, 'distance'):
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det_geo['distance'] = t.Tensor(det_geo['distance'])
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if hasattr(det_geo, 'basis'):
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det_geo['basis'] = t.Tensor(det_geo['basis'])
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if hasattr(det_geo, 'corner'):
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det_geo['corner'] = t.Tensor(det_geo['corner'])
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self.min_translation = t.Tensor(min_translation)
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self.probe_basis = t.Tensor(probe_basis)
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self.detector_slice = detector_slice
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self.surface_normal = t.Tensor(surface_normal)
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self.saturation = saturation
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if mask is None:
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self.mask = mask
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else:
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self.mask = t.BoolTensor(mask)
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# We rescale the probe here so it learns at the same rate as the
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# object
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if probe_guess.dim() > 3:
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self.probe_norm = 1 * t.max(tools.cmath.cabs(probe_guess[0].to(t.float32)))
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else:
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self.probe_norm = 1 * t.max(tools.cmath.cabs(probe_guess.to(t.float32)))
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self.probe = t.nn.Parameter(probe_guess.to(t.float32)
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/ self.probe_norm)
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self.obj = t.nn.Parameter(obj_guess.to(t.float32))
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if background is None:
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if detector_slice is not None:
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background = 1e-6 * t.ones(self.probe[0][self.detector_slice].shape[:-1])
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else:
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background = 1e-6 * t.ones(self.probe[0].shape[:-1])
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self.background = t.nn.Parameter(t.Tensor(background).to(t.float32))
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if weights is None:
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self.weights = None
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else:
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self.weights = t.nn.Parameter(t.Tensor(weights).to(t.float32))
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if translation_offsets is None:
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self.translation_offsets = None
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else:
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self.translation_offsets = t.nn.Parameter(t.Tensor(translation_offsets).to(t.float32)/ translation_scale)
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self.translation_scale = translation_scale
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if probe_support is not None:
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self.probe_support = probe_support
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else:
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self.probe_support = t.ones_like(self.probe[0])
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if obj_support is not None:
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self.obj_support = obj_support
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self.obj.data = self.obj * obj_support
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else:
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self.obj_support = t.ones_like(self.obj)
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self.oversampling = oversampling
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spacing = np.linalg.norm(self.probe_basis,axis=0)
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shape = np.array(self.probe.shape[1:-1])
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self.as_prop = tools.propagators.generate_angular_spectrum_propagator(shape, spacing, self.wavelength, self.dz)
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@classmethod
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def from_dataset(cls, dataset, dz, nz, probe_size=None, randomize_ang=0, padding=0, n_modes=1, translation_scale = 1, saturation=None, probe_support_radius=None, propagation_distance=None, restrict_obj=-1, scattering_mode=None, oversampling=1, auto_center=True):
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wavelength = dataset.wavelength
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det_basis = dataset.detector_geometry['basis']
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det_shape = dataset[0][1].shape
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distance = dataset.detector_geometry['distance']
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# always do this on the cpu
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get_as_args = dataset.get_as_args
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dataset.get_as(device='cpu')
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(indices, translations), patterns = dataset[:]
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dataset.get_as(*get_as_args[0],**get_as_args[1])
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# Set to none to avoid issues with things outside the detector
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if auto_center:
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center = tools.image_processing.centroid(t.sum(patterns,dim=0))
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else:
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center = None
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# Then, generate the probe geometry from the dataset
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ewg = tools.initializers.exit_wave_geometry
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probe_basis, probe_shape, det_slice = ewg(det_basis,
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det_shape,
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wavelength,
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distance,
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center=center,
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padding=padding,
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opt_for_fft=False,
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oversampling=oversampling)
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if hasattr(dataset, 'sample_info') and \
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dataset.sample_info is not None and \
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'orientation' in dataset.sample_info:
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surface_normal = dataset.sample_info['orientation'][2]
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else:
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surface_normal = np.array([0.,0.,1.])
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# If this information is supplied when the function is called,
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# then we override the information in the .cxi file
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if scattering_mode in {'t', 'transmission'}:
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surface_normal = np.array([0.,0.,1.])
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elif scattering_mode in {'r', 'reflection'}:
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outgoing_dir = np.cross(det_basis[:,0], det_basis[:,1])
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outgoing_dir /= np.linalg.norm(outgoing_dir)
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surface_normal = outgoing_dir + np.array([0.,0.,1.])
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surface_normal /= np.linalg.norm(outgoing_dir)
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# Next generate the object geometry from the probe geometry and
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# the translations
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pix_translations = tools.interactions.translations_to_pixel(probe_basis, translations, surface_normal=surface_normal)
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obj_size, min_translation = tools.initializers.calc_object_setup(probe_shape, pix_translations, padding=200)
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if hasattr(dataset, 'background') and dataset.background is not None:
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background = t.sqrt(dataset.background)
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else:
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background = None
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# Finally, initialize the probe and object using this information
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if probe_size is None:
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probe = tools.initializers.SHARP_style_probe(dataset, probe_shape, det_slice, propagation_distance=propagation_distance, oversampling=oversampling)
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else:
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probe = tools.initializers.gaussian_probe(dataset, probe_basis, probe_shape, probe_size, propagation_distance=propagation_distance)
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# Now we initialize all the subdominant probe modes
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probe_max = t.max(cmath.cabs(probe))
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probe_stack = [0.01 * probe_max * t.rand(probe.shape,dtype=probe.dtype) for i in range(n_modes - 1)]
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probe = t.stack([probe,] + probe_stack)
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obj = tools.cmath.expi(randomize_ang * (t.rand(obj_size)-0.5))
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det_geo = dataset.detector_geometry
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translation_offsets = 0 * (t.rand((len(dataset),2)) - 0.5)
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weights = t.ones(len(dataset))
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if hasattr(dataset, 'mask') and dataset.mask is not None:
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mask = dataset.mask.to(t.bool)
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else:
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mask = None
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if probe_support_radius is not None:
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probe_support = t.zeros_like(probe[0].to(dtype=t.float32))
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p_cent = np.array(probe.shape[1:3]).astype(int) // 2
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psr = int(probe_support_radius)
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probe_support[p_cent[0]-psr:p_cent[0]+psr,
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p_cent[1]-psr:p_cent[1]+psr] = 1
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probe = probe * probe_support[None,:,:]
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else:
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probe_support = None;
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if restrict_obj != -1:
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ro = restrict_obj
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os = np.array(obj_size)
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ps = np.array(probe_shape)
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obj_support = t.zeros_like(obj.to(dtype=t.float32))
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obj_support[ps[0]//2-ro:os[0]+ro-ps[0]//2,
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ps[1]//2-ro:os[1]+ro-ps[1]//2] = 1
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else:
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obj_support = None
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return cls(wavelength, det_geo, probe_basis, probe, obj, dz, nz,
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detector_slice=det_slice,
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surface_normal=surface_normal,
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min_translation=min_translation,
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translation_offsets = translation_offsets,
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weights=weights, mask=mask, background=background,
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translation_scale=translation_scale,
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saturation=saturation,
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probe_support=probe_support,
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obj_support=obj_support,
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oversampling=oversampling)
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def interaction(self, index, translations):
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pix_trans = tools.interactions.translations_to_pixel(self.probe_basis,
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translations,
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surface_normal=self.surface_normal)
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pix_trans -= self.min_translation
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if self.translation_offsets is not None:
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pix_trans += self.translation_scale * self.translation_offsets[index]
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all_exit_waves = []
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for i in range(self.probe.shape[0]):
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pr = self.probe[i] * self.probe_support
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#exit_waves = pr
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#print(self.probe_norm)
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#for i in range(self.nz):
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exit_waves = []
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if len(pix_trans.shape) == 1:
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pix_trans = [pix_trans]
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for trans in pix_trans:
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exit_wave = self.probe_norm * pr
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for i in range(self.nz-1):
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#exit_wave = tools.interactions.ptycho_2D_sinc(exit_wave,
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# self.obj_support * self.obj,
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# trans,
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# shift_probe=True)
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exit_wave = tools.interactions.ptycho_2D_round(exit_wave,
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self.obj_support * cmath.cexpi(self.obj.data/self.nz),
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trans)
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exit_wave = tools.propagators.near_field(exit_wave,self.as_prop)
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#tools.plotting.plot_amplitude(exit_wave)
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#plt.show()
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# only final layer gets a derivative
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exit_wave = tools.interactions.ptycho_2D_round(exit_wave,
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self.obj_support * cmath.cexpi(0.1*self.obj),
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trans)
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exit_waves.append(exit_wave)
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exit_waves = t.stack(exit_waves)
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if np.array(index).size == 1:
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index = [index]
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strip_first_index = True
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else:
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strip_first_index = False
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if exit_waves.dim() == 4:
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exit_waves = self.weights[index][:,None,None,None] * exit_waves
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else:
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exit_waves = self.weights[index] * exit_waves
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if strip_first_index:
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exit_waves = exit_waves[0,...]
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all_exit_waves.append(exit_waves)
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return t.stack(all_exit_waves)
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def forward_propagator(self, wavefields):
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return tools.propagators.far_field(wavefields)
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def backward_propagator(self, wavefields):
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return tools.propagators.inverse_far_field(wavefields)
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def measurement(self, wavefields):
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return tools.measurements.quadratic_background(wavefields,
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self.background,
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detector_slice=self.detector_slice,
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measurement=tools.measurements.incoherent_sum,
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saturation=self.saturation,
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oversampling=self.oversampling)
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def loss(self, sim_data, real_data, mask=None):
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regularizer = t.mean(t.abs(self.obj))
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loss = tools.losses.amplitude_mse(real_data, sim_data, mask=mask)
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lambd = 1
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return loss + lambd * regularizer
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#return tools.losses.poisson_nll(real_data, sim_data, mask=mask)
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def to(self, *args, **kwargs):
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super(Multislice2DPtycho, self).to(*args, **kwargs)
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self.wavelength = self.wavelength.to(*args,**kwargs)
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# move the detector geometry too
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det_geo = self.detector_geometry
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if hasattr(det_geo, 'distance'):
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det_geo['distance'] = det_geo['distance'].to(*args,**kwargs)
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if hasattr(det_geo, 'basis'):
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det_geo['basis'] = det_geo['basis'].to(*args,**kwargs)
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if hasattr(det_geo, 'corner'):
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det_geo['corner'] = det_geo['corner'].to(*args,**kwargs)
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if self.mask is not None:
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self.mask = self.mask.to(*args, **kwargs)
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self.min_translation = self.min_translation.to(*args,**kwargs)
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self.probe_basis = self.probe_basis.to(*args,**kwargs)
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self.probe_norm = self.probe_norm.to(*args,**kwargs)
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self.probe_support = self.probe_support.to(*args,**kwargs)
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self.obj_support = self.obj_support.to(*args,**kwargs)
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self.surface_normal = self.surface_normal.to(*args, **kwargs)
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self.as_prop = self.as_prop.to(*args, **kwargs)
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def sim_to_dataset(self, args_list):
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# In the future, potentially add more control
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# over what metadata is saved (names, etc.)
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# First, I need to gather all the relevant data
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# that needs to be added to the dataset
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entry_info = {'program_name': 'CDTools',
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'instrument_n': 'Simulated Data',
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'start_time': datetime.now()}
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surface_normal = self.surface_normal.detach().cpu().numpy()
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xsurfacevec = np.cross(np.array([0.,1.,0.]), surface_normal)
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xsurfacevec /= np.linalg.norm(xsurfacevec)
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ysurfacevec = np.cross(surface_normal, xsurfacevec)
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ysurfacevec /= np.linalg.norm(ysurfacevec)
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orientation = np.array([xsurfacevec, ysurfacevec, surface_normal])
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sample_info = {'description': 'A simulated sample',
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'orientation': orientation}
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detector_geometry = self.detector_geometry
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mask = self.mask
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wavelength = self.wavelength
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indices, translations = args_list
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# Then we simulate the results
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data = self.forward(indices, translations)
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# And finally, we make the dataset
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return Ptycho2DDataset(translations, data,
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entry_info = entry_info,
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sample_info = sample_info,
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wavelength=wavelength,
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detector_geometry=detector_geometry,
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mask=mask)
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def corrected_translations(self,dataset):
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translations = dataset.translations.to(dtype=self.probe.dtype,device=self.probe.device)
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t_offset = tools.interactions.pixel_to_translations(self.probe_basis,self.translation_offsets*self.translation_scale,surface_normal=self.surface_normal)
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return translations + t_offset
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# Needs to be updated to allow for plotting to an existing figure
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plot_list = [
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('Dominant Probe Amplitude',
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lambda self, fig: p.plot_amplitude(self.probe[0], fig=fig, basis=self.probe_basis)),
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('Dominant Probe Phase',
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lambda self, fig: p.plot_phase(self.probe[0], fig=fig, basis=self.probe_basis)),
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('Subdominant Probe Amplitude',
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lambda self, fig: p.plot_amplitude(self.probe[1], fig=fig, basis=self.probe_basis),
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lambda self: len(self.probe) >=2),
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('Subdominant Probe Phase',
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lambda self, fig: p.plot_phase(self.probe[1], fig=fig, basis=self.probe_basis),
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lambda self: len(self.probe) >=2),
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('Object Amplitude',
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lambda self, fig: p.plot_amplitude(self.obj, fig=fig, basis=self.probe_basis)),
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('Object Phase',
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lambda self, fig: p.plot_phase(self.obj, fig=fig, basis=self.probe_basis)),
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('Corrected Translations',
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lambda self, fig, dataset: p.plot_translations(self.corrected_translations(dataset), fig=fig)),
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('Background',
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lambda self, fig: plt.figure(fig.number) and plt.imshow(self.background.detach().cpu().numpy()**2))
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]
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def save_results(self, dataset):
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basis = self.probe_basis.detach().cpu().numpy()
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translations = self.corrected_translations(dataset).detach().cpu().numpy()
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probe = cmath.torch_to_complex(self.probe.detach().cpu())
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probe = probe * self.probe_norm.detach().cpu().numpy()
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obj = cmath.torch_to_complex(self.obj.detach().cpu())
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background = self.background.detach().cpu().numpy()**2
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weights = self.weights.detach().cpu().numpy()
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dz = self.dz
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nz = self.nz
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prop = self.as_prop
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return {'basis':basis, 'translation':translations,
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'probe':probe,'obj':obj,
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'background':background,
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'weights':weights, 'dz':dz, 'nz':nz,
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'interlayer propagator': prop}
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