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474 lines
17 KiB
Python
474 lines
17 KiB
Python
"""This module contains all the models for different CDI Reconstructions
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All the reconstructions are coordinated through the ptychography models
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defined here. The models are, at their core, just subclasses of the
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:code:`torch.nn.model` class, so they contain the same structure of
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parameters, etc. Their central functionality is as a simulation that maps
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some input (usually, the index number of a scan point) to an output that
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corresponds to the measured data (usually, a diffraction pattern). This
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model can then be used as the heart of an automatic differentiation
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reconstruction which retrieves the parameters that were used in the model.
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The subclasses of the main CDIModel class are required to define their
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own implementations of the following functions:
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Loading and Saving
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------------------
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from_dataset
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Creates a CDIModel from an appropriate CDataset
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simulate_to_dataset
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Creates a CDataset from the simulation defined in the model
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save_results
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Saves out a dictionary with the recovered parameters
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Simulation
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----------
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interaction
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Simulates exit waves from experimental parameters
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forward_propagator
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The propagator from the experiment plane to the detector plane
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backward_propagator
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Optional, the propagator from the detector plane to the experiment plane
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measurement
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Simulates the detector readout from a detector plane wavefront
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loss
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the loss function to report and use for automatic differentiation
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Example implementations of all these functions can be found in the code
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for the SimplePtycho class.
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In addition, it is recommended to read through the tutorial section on
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defining a new ptychography model before attempting to do so
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"""
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from __future__ import division, print_function, absolute_import
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import torch as t
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from torch.utils import data as torchdata
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from matplotlib import pyplot as plt
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from matplotlib.widgets import Slider
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from matplotlib import ticker
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import numpy as np
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__all__ = ['CDIModel', 'SimplePtycho', 'FancyPtycho', 'Bragg2DPtycho']
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class CDIModel(t.nn.Module):
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"""This base model defines all the functions that must be exposed for a valid CDIModel subclass
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Most of the functions only raise a NotImplementedError at this level and
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must be explicitly defined by any subclass - these are noted explocitly
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in the module-level intro. The work of defining the various subclasses
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boils down to creating an appropriate implementation for this set of
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functions.
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"""
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def from_dataset(self, dataset):
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raise NotImplementedError()
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def interaction(self, *args):
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raise NotImplementedError()
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def forward_propagator(self, exit_wave):
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raise NotImplementedError()
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def backward_propagator(self, detector_wave):
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raise NotImplementedError()
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def measurement(self, detector_wave):
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raise NotImplementedError()
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def forward(self, *args):
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"""The complete forward model
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This model relies on composing the interaction, forward propagator,
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and measurement functions which are required to be defined by all
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subclasses. It therefore should not be redefined by the subclasses.
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The arguments to this function, for any given subclass, will be
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the same as the arguments to the interaction function.
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"""
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return self.measurement(self.forward_propagator(self.interaction(*args)))
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def loss(self, sim_data, real_data):
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raise NotImplementedError()
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def to(self, *args, **kwargs):
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super(CDIModel,self).to(*args,**kwargs)
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def simulate_to_dataset(self, args_list):
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raise NotImplementedError()
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def save_results(self):
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raise NotImplementedError()
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def AD_optimize(self, iterations, data_loader, optimizer, scheduler=None):
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"""Runs a round of reconstruction using the provided optimizer
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This is the basic automatic differentiation reconstruction tool
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which all the other, algorithm-specific tools, use.
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Like all the other optimization routines, it is defined as a
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generator function which yields the average loss each epoch.
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Parameters
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----------
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iterations : int
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How many epochs of the algorithm to run
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dataset : CDataset
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The dataset to reconstruct against
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optimizer : torch.optim.Optimizer
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The optimizer to run the reconstruction with
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scheduler : torch.optim.lr_scheduler._LRScheduler
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Optional, a learning rate scheduler to use
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"""
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# First, calculate the normalization
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normalization = 0
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for inputs, patterns in data_loader:
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normalization += t.sum(patterns).cpu().numpy()
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for it in range(iterations):
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loss = 0
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N = 0
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for inputs, patterns in data_loader:
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N += 1
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def closure():
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optimizer.zero_grad()
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sim_patterns = self.forward(*inputs)
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if hasattr(self, 'mask'):
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loss = self.loss(patterns,sim_patterns, mask=self.mask)
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else:
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loss = self.loss(patterns,sim_patterns)
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loss.backward()
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return loss
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loss += optimizer.step(closure).detach().cpu().numpy()
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loss /= normalization
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if scheduler is not None:
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scheduler.step(loss)
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yield loss
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def Adam_optimize(self, iterations, dataset, batch_size=15, lr=0.005, schedule=False, amsgrad=False):
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"""Runs a round of reconstruction using the Adam optimizer
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This is generally accepted to be the most robust algorithm for use
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with ptychography. Like all the other optimization routines,
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it is defined as a generator function, which yields the average
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loss each epoch.
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Parameters
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----------
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iterations : int
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How many epochs of the algorithm to run
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dataset : CDataset
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The dataset to reconstruct against
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batch_size : int
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Optional, the size of the minibatches to use
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lr : float
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Optional, The learning rate (alpha) to use
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schedule : float
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Optional, whether to use the ReduceLROnPlateau scheduler
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"""
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# Make a dataloader
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data_loader = torchdata.DataLoader(dataset, batch_size=batch_size,
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shuffle=True)
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# Define the optimizer
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optimizer = t.optim.Adam(self.parameters(), lr = lr, amsgrad=amsgrad)
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# Define the scheduler
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if schedule:
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scheduler = t.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2,threshold=1e-9)
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else:
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scheduler = None
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return self.AD_optimize(iterations, data_loader, optimizer, scheduler=scheduler)
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def LBFGS_optimize(self, iterations, dataset, batch_size=None,
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lr=0.1,history_size=2):
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"""Runs a round of reconstruction using the L-BFGS optimizer
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This algorithm is often less stable that Adam, however in certain
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situations or geometries it can be shockingly efficient. Like all
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the other optimization routines, it is defined as a generator
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function which yields the average loss each epoch.
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Parameters
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----------
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iterations : int
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How many epochs of the algorithm to run
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dataset : CDataset
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The dataset to reconstruct against
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batch_size : int
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Optional, the size of the minibatches to use
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lr : float
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Optional, the learning rate to use
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history_size : int
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Optional, the length of the history to use.
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"""
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# Make a dataloader
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if batch_size is not None:
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data_loader = torchdata.DataLoader(dataset, batch_size=batch_size,
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shuffle=True)
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else:
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data_loader = torchdata.DataLoader(dataset)
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# Define the optimizer
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optimizer = t.optim.LBFGS(self.parameters(),
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lr = lr, history_size=history_size)
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return self.AD_optimize(iterations, data_loader, optimizer)
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def SGD_optimize(self, iterations, dataset, batch_size=None,
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lr=0.01, momentum=0, dampening=0, weight_decay=0,
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nesterov=False):
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"""Runs a round of reconstruction using the SGDoptimizer
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This algorithm is often less stable that Adam, but it is simpler
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and is the basic workhorse of gradience descent.
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Parameters
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----------
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iterations : int
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How many epochs of the algorithm to run
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dataset : CDataset
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The dataset to reconstruct against
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batch_size : int
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Optional, the size of the minibatches to use
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lr : float
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Optional, the learning rate to use
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momentum : float
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Optional, the length of the history to use.
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"""
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# Make a dataloader
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if batch_size is not None:
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data_loader = torchdata.DataLoader(dataset, batch_size=batch_size,
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shuffle=True)
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else:
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data_loader = torchdata.DataLoader(dataset)
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# Define the optimizer
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optimizer = t.optim.SGD(self.parameters(),
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lr = lr, momentum=momentum,
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dampening=dampening,
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weight_decay=weight_decay,
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nesterov=nesterov)
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return self.AD_optimize(iterations, data_loader, optimizer)
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# By default, the plot_list is empty
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plot_list = []
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def inspect(self, dataset=None, update=True):
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"""Plots all the plots defined in the model's plot_list attribute
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If update is set to True, it will update any previously plotted set
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of plots, if one exists, and then redraw them. Otherwise, it will
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plot a new set, and any subsequent updates will update the new set
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Optionally, a dataset can be passed, which then will plot any
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registered plots which need to incorporate some information from
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the dataset (such as geometry or a comparison with measured data).
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Plots can be registered in any subclass by defining the plot_list
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attribute. This should be a list of tuples in the following format:
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( 'Plot Title', function_to_generate_plot(self),
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function_to_determine_whether_to_plot(self))
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Where the third element in the tuple (a function that returns
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True if the plot is relevant) is not required.
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Parameters
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----------
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dataset : CDataset
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Optional, a dataset matched to the model type
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update : bool
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Whether to update existing plots or plot new ones
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"""
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first_update = False
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if update and hasattr(self, 'figs') and self.figs:
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figs = self.figs
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elif update:
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figs = None
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self.figs = []
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first_update = True
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else:
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figs = None
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self.figs = []
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idx = 0
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for plots in self.plot_list:
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# If a conditional is included in the plot
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try:
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if len(plots) >=3 and not plots[2](self):
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continue
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except TypeError as e:
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if len(plots) >= 3 and not plots[2](self, dataset):
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continue
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name = plots[0]
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plotter = plots[1]
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if figs is None:
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fig = plt.figure()
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self.figs.append(fig)
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else:
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fig = figs[idx]
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try:
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plotter(self,fig)
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plt.title(name)
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except TypeError as e:
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if dataset is not None:
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try:
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plotter(self, fig, dataset)
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plt.title(name)
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except (IndexError, KeyError, AttributeError) as e:
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pass
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except (IndexError, KeyError, AttributeError) as e:
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pass
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idx += 1
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if update:
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plt.draw()
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fig.canvas.start_event_loop(0.001)
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if first_update:
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plt.pause(0.05 * len(self.figs))
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def compare(self, dataset):
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"""Opens a tool for comparing simulated and measured diffraction patterns
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Parameters
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----------
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dataset : CDataset
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A dataset containing the simulated diffraction patterns to compare against
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"""
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fig, axes = plt.subplots(1,3,figsize=(12,5.3))
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fig.tight_layout(rect=[0.02, 0.09, 0.98, 0.96])
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axslider = plt.axes([0.15,0.06,0.75,0.03])
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def update_colorbar(im):
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# If the update brought the colorbar out of whack
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# (say, from clicking back in the navbar)
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# Holy fuck this was annoying. Sorry future for how
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# crappy this solution is.
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#if not np.allclose(im.colorbar.ax.get_xlim(),
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# (np.min(im.get_array()),
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# np.max(im.get_array()))):
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if hasattr(im, 'norecurse') and im.norecurse:
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im.norecurse=False
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return
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im.norecurse=True
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im.colorbar.set_clim(vmin=np.min(im.get_array()),vmax=np.max(im.get_array()))
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im.colorbar.ax.set_ylim(0,1)
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im.colorbar.set_ticks(ticker.LinearLocator(numticks=5))
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im.colorbar.draw_all()
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def update(idx):
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idx = int(idx) % len(dataset)
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fig.pattern_idx = idx
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updating = True if len(axes[0].images) >= 1 else False
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inputs, output = dataset[idx]
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sim_data = self.forward(*inputs).detach().cpu().numpy()
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sim_data = sim_data
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meas_data = output.detach().cpu().numpy()
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if hasattr(self, 'mask') and self.mask is not None:
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mask = self.mask.detach().cpu().numpy()
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else:
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mask = 1
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if not updating:
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axes[0].set_title('Simulated')
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axes[1].set_title('Measured')
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axes[2].set_title('Difference')
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sim = axes[0].imshow(sim_data)
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meas = axes[1].imshow(meas_data * mask)
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diff = axes[2].imshow((sim_data-meas_data) * mask)
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cb1 = plt.colorbar(sim, ax=axes[0], orientation='horizontal',format='%.2e',ticks=ticker.LinearLocator(numticks=5),pad=0.1,fraction=0.1)
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cb1.ax.tick_params(labelrotation=20)
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cb1.ax.callbacks.connect('xlim_changed', lambda ax: update_colorbar(sim))
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cb2 = plt.colorbar(meas, ax=axes[1], orientation='horizontal',format='%.2e',ticks=ticker.LinearLocator(numticks=5),pad=0.1,fraction=0.1)
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cb2.ax.tick_params(labelrotation=20)
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cb2.ax.callbacks.connect('xlim_changed', lambda ax: update_colorbar(meas))
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cb3 = plt.colorbar(diff, ax=axes[2], orientation='horizontal',format='%.2e',ticks=ticker.LinearLocator(numticks=5),pad=0.1,fraction=0.1)
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cb3.ax.tick_params(labelrotation=20)
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cb3.ax.callbacks.connect('xlim_changed', lambda ax: update_colorbar(diff))
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else:
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sim = axes[0].images[-1]
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sim.set_data(sim_data)
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update_colorbar(sim)
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meas = axes[1].images[-1]
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meas.set_data(meas_data * mask)
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update_colorbar(meas)
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diff = axes[2].images[-1]
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diff.set_data((sim_data-meas_data) * mask)
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update_colorbar(diff)
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# This is dumb but the slider doesn't work unless a reference to it is
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# kept somewhere...
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self.slider = Slider(axslider, 'Pattern #', 0, len(dataset)-1, valstep=1, valfmt="%d")
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self.slider.on_changed(update)
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def on_action(event):
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if not hasattr(event, 'button'):
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event.button = None
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if not hasattr(event, 'key'):
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event.key = None
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if event.key == 'up' or event.button == 'up':
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update(fig.pattern_idx - 1)
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elif event.key == 'down' or event.button == 'down':
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update(fig.pattern_idx + 1)
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self.slider.set_val(fig.pattern_idx)
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plt.draw()
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fig.canvas.mpl_connect('key_press_event',on_action)
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fig.canvas.mpl_connect('scroll_event',on_action)
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update(0)
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from CDTools.models.simple_ptycho import SimplePtycho
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from CDTools.models.fancy_ptycho import FancyPtycho
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from CDTools.models.pinhole_plane_ptycho import PinholePlanePtycho
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from CDTools.models.bragg_2d_ptycho import Bragg2DPtycho
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