def __init__(self, graph, get_output=False, output_dims=2, scheduler=None, copy_inputs=False):
self.output_tasks = []
self.sched = scheduler if scheduler else Ufo.Scheduler()
self._started = False
self.copy_inputs = copy_inputs
if isinstance(graph, Ufo.TaskGraph):
self.graph = graph
roots = self.graph.get_roots()
elif isinstance(graph, Ufo.TaskNode):
self.graph = Ufo.TaskGraph()
roots = [graph]
else:
msg = 'graph is neither Ufo.TaskGraph nor Ufo.TaskNode'
raise ValueError(msg)
# Initialize inputs
self.input_tasks = {}
self.ufo_buffers = {}
for root in roots:
self.input_tasks[root] = []
self.ufo_buffers[root] = []
num_inputs = root.get_num_inputs()
for i in range(num_inputs):
self.input_tasks[root].append(Ufo.InputTask())
self.ufo_buffers[root].append(None)
self.graph.connect_nodes_full(self.input_tasks[root][i], root, i)
if get_output:
for i, leave in enumerate(self.graph.get_leaves()):
self.output_tasks.append(Ufo.OutputTask())
self.output_tasks[-1].props.num_dims = output_dims
self.graph.connect_nodes(leave, self.output_tasks[-1])
def _run(params, x_region, y_region, regions, index):
"""Execute one pass on all possible GPUs with slice ranges given by *regions*."""
from gi.repository import Ufo
pm = Ufo.PluginManager()
graph = Ufo.TaskGraph()
scheduler = Ufo.FixedScheduler()
gpus = scheduler.get_resources().get_gpu_nodes()
num_gpus = len(gpus)
broadcast = Ufo.CopyTask()
source = _setup_source(params, pm, graph)
graph.connect_nodes(source, broadcast)
for i, region in enumerate(regions):
subindex = index * num_gpus + i
_setup_graph(pm,
graph,
subindex,
x_region,
y_region,
region,
params,
broadcast,
gpu=gpus[i])
scheduler.run(graph)
duration = scheduler.props.time
LOG.info('Execution time: {} s'.format(duration))
return duration
def measure_ufo(out_path, metric, axis, width, height):
pm = Ufo.PluginManager()
sched = Ufo.Scheduler()
graph = Ufo.TaskGraph()
input_path = 'data/measure.tif'
image = make_input(width, height)
tifffile.imsave(input_path, image)
reader = pm.get_task('read')
measure = pm.get_task('measure')
output = Ufo.OutputTask()
reader.props.path = input_path
measure.props.axis = axis
measure.props.metric = metric
graph.connect_nodes(reader, measure)
graph.connect_nodes(measure, output)
sched.run(graph)
buf = output.get_output_buffer()
gpu_result = ufo.numpy.asarray(buf)
write_image(out_path, gpu_result)
def __init__(self, graph, get_output=False):
self.output_task = None
self._started = False
if isinstance(graph, Ufo.TaskGraph):
self.graph = graph
roots = self.graph.get_roots()
elif isinstance(graph, Ufo.TaskNode):
self.graph = Ufo.TaskGraph()
roots = [graph]
else:
msg = 'graph is neither Ufo.TaskGraph nor Ufo.TaskNode'
raise ValueError(msg)
# Initialize inputs
self.input_tasks = {}
self.ufo_buffers = {}
for root in roots:
self.input_tasks[root] = []
self.ufo_buffers[root] = []
num_inputs = root.get_num_inputs()
for i in range(num_inputs):
self.input_tasks[root].append(Ufo.InputTask())
self.ufo_buffers[root].append(None)
self.graph.connect_nodes_full(self.input_tasks[root][i], root,
i)
if get_output:
self.output_task = Ufo.OutputTask()
leaves = self.graph.get_leaves()
self.graph.connect_nodes(leaves[0], self.output_task)
def compute_phase_spectrum_density(args):
graph = Ufo.TaskGraph()
scheduler = Ufo.Scheduler()
build_power_spectrum_density_pipeline(args, graph, scheduler)
# scheduler.run(graph) # runned inside for now
duration = scheduler.props.time
return duration
def run_flat_correct(args):
graph = Ufo.TaskGraph()
sched = Ufo.Scheduler()
pm = Ufo.PluginManager()
out_task = get_writer(args)
flat_task = create_flat_correct_pipeline(args, graph)
graph.connect_nodes(flat_task, out_task)
sched.run(graph)
def generate_partial(append=False):
graph = Ufo.TaskGraph()
sched = Ufo.Scheduler()
args.output_append = append
writer = get_writer(args)
sinos = create_sinogram_pipeline(args, graph)
graph.connect_nodes(sinos, writer)
sched.run(graph)
def run_flat_correct(args):
graph = Ufo.TaskGraph()
sched = Ufo.Scheduler()
pm = Ufo.PluginManager()
out_task = pm.get_task('write')
out_task.props.filename = args.output
flat_task = create_flat_correct_pipeline(args, graph)
graph.connect_nodes(flat_task, out_task)
sched.run(graph)
def run_preprocessing(args):
graph = Ufo.TaskGraph()
sched = Ufo.Scheduler()
pm = Ufo.PluginManager()
out_task = get_writer(args)
current = create_preprocessing_pipeline(args, graph)
graph.connect_nodes(current, out_task)
sched.run(graph)
def generate_partial(append=False):
pm = Ufo.PluginManager()
graph = Ufo.TaskGraph()
sched = Ufo.Scheduler()
writer = pm.get_task('write')
writer.props.filename = args.output
writer.props.append = append
sinos = create_sinogram_pipeline(args, graph)
graph.connect_nodes(sinos, writer)
sched.run(graph)
def __init__(self, graph):
self.input_task = Ufo.InputTask()
if isinstance(graph, Ufo.TaskGraph):
self.graph = graph
roots = self.graph.get_roots()
self.graph.connect_nodes(self.input_task, roots[0])
elif isinstance(graph, Ufo.TaskNode):
self.graph = Ufo.TaskGraph()
self.graph.connect_nodes(self.input_task, graph)
else:
msg = 'graph is neither Ufo.TaskGraph nor Ufo.TaskNode'
raise ValueError(msg)
self.ufo_buffer = None
def test_wrong_connection():
from ufo import Read, FlatFieldCorrect, Write
darks = Read()
flats = Read()
radios = Read()
write = Write()
ffc = FlatFieldCorrect()
g = Ufo.TaskGraph()
g.connect_nodes_full(radios.task, ffc.task, 0)
g.connect_nodes_full(darks.task, ffc.task, 1)
g.connect_nodes_full(flats.task, ffc.task, 0)
g.connect_nodes(ffc.task, write.task)
sched = Ufo.Scheduler()
sched.run(g)
def initialize(self):
self.data = self.get("DataStore").data()
self.graph = Ufo.TaskGraph()
self.sched = Ufo.Scheduler()
manager = Ufo.PluginManager()
self.read = manager.get_task('memory-in')
self.sino = manager.get_task('transpose-projections')
self.pad = manager.get_task('pad')
self.fft = manager.get_task('fft')
self.fltr = manager.get_task('filter')
self.ifft = manager.get_task('ifft')
self.bp = manager.get_task('backproject')
self.crop = manager.get_task('crop')
self.write= manager.get_task('memory-out')
self.LogInfo("initialized, UFO Reconstruction")
return True
def start_one(index):
gpu_index, region = regions[index]
scheduler = Ufo.FixedScheduler()
scheduler.set_resources(resources[index])
graph = Ufo.TaskGraph()
gpu = scheduler.get_resources().get_gpu_nodes()[gpu_index]
region_index = run_number * len(resources) + index
geometry = CTGeometry(args)
if (len(args.center_position_z) == 1
and np.modf(args.center_position_z[0])[0] == 0
and geometry.is_simple_parallel_tomo):
LOG.info(
'Simple tomography with integer z center, changing to center_position_z + 0.5 '
'to avoid interpolation')
geometry.args.center_position_z = (
geometry.args.center_position_z[0] + 0.5, )
if not args.disable_projection_crop:
if not args.dry_run and (args.y or args.height):
LOG.debug(
'--y or --height specified, not optimizing projection region'
)
else:
geometry.optimize_args(region=region)
opt_args = geometry.args
if args.dry_run:
source = get_task('dummy-data',
number=args.number,
width=args.width,
height=args.height)
else:
source = None
setup_graph(opt_args,
graph,
x_region,
y_region,
region,
source=source,
gpu=gpu,
index=region_index,
make_reader=True)
LOG.debug('Pass: %d, device: %d, region: %s', run_number + 1,
gpu_index, region)
scheduler.run(graph)
return scheduler.props.time
def __init__(self, axis_pos=None):
self.pm = PluginManager()
self.fft = self.pm.get_task('fft', dimensions=1)
self.ifft = self.pm.get_task('ifft', dimensions=1)
self.fltr = self.pm.get_task('filter')
self.backprojector = self.pm.get_task('backproject')
if axis_pos:
self.backprojector.props.axis_pos = axis_pos
graph = Ufo.TaskGraph()
graph.connect_nodes(self.fft, self.fltr)
graph.connect_nodes(self.fltr, self.ifft)
graph.connect_nodes(self.ifft, self.backprojector)
super(Backproject, self).__init__(graph, get_output=True)
self.output_task.props.num_dims = 2
def ufo_dfi(tomo, center, recon, theta, **kwargs):
"""
Reconstruct object using UFO's Direct Fourier pipeline
"""
import gi
gi.require_version('Ufo', '0.0')
from gi.repository import Ufo
theta = theta[1] - theta[0]
center = center[0]
g = Ufo.TaskGraph()
pm = Ufo.PluginManager()
sched = Ufo.Scheduler()
input_task = Ufo.InputTask()
output_task = Ufo.OutputTask()
pad = pm.get_task('zeropad')
fft = pm.get_task('fft')
ifft = pm.get_task('ifft')
dfi = pm.get_task('dfi-sinc')
swap_forward = pm.get_task('swap-quadrants')
swap_backward = pm.get_task('swap-quadrants')
pad.set_properties(oversampling=1, center_of_rotation=center)
fft.set_properties(dimensions=1, auto_zeropadding=False)
ifft.set_properties(dimensions=2)
dfi.set_properties(angle_step=theta)
g.connect_nodes(input_task, pad)
g.connect_nodes(pad, fft)
g.connect_nodes(fft, dfi)
g.connect_nodes(dfi, swap_forward)
g.connect_nodes(swap_forward, ifft)
g.connect_nodes(ifft, swap_backward)
g.connect_nodes(swap_backward, output_task)
args = (input_task, output_task, tomo, recon)
thread = threading.Thread(target=_process_data, args=args)
thread.start()
sched.run(g)
thread.join()
logger.info("UFO+DFI run time: {}s".format(sched.props.time))
def transpose_ufo(width, height, path):
pm = Ufo.PluginManager()
graph = Ufo.TaskGraph()
sched = Ufo.Scheduler()
input_path = 'data/transpose.tif'
image = make_input(width, height)
tifffile.imsave(input_path, image)
reader = pm.get_task('read')
transpose = pm.get_task('transpose')
writer = pm.get_task('write')
reader.props.path = input_path
writer.props.filename = path
graph.connect_nodes(reader, transpose)
graph.connect_nodes(transpose, writer)
sched.run(graph)
def test_core_issue_64_fixed_expansion():
g = Ufo.TaskGraph()
pm = Ufo.PluginManager()
sched = Ufo.FixedScheduler()
arch = Ufo.ArchGraph()
gpus = arch.get_gpu_nodes()
sched.set_gpu_nodes(arch, gpus)
generate = pm.get_task('generate')
null = pm.get_task('null')
generate.set_properties(number=5, width=512, height=512)
for gpu in gpus:
median = pm.get_task('median-filter')
median.set_proc_node(gpu)
g.connect_nodes(generate, median)
g.connect_nodes(median, null)
sched.run(g)
def test_broadcast():
from ufo import Generate, Writer
import glob
with tempdir() as d:
generate = Generate(number=5, width=512, height=512)
write1 = Writer(filename=d.path('foo-%i.tif'))
write2 = Writer(filename=d.path('bar-%i.tif'))
g = Ufo.TaskGraph()
g.connect_nodes(generate.task, write1.task)
g.connect_nodes(generate.task, write2.task)
sched = Ufo.Scheduler()
sched.run(g)
foos = glob.glob(d.path('foo-*'))
bars = glob.glob(d.path('bar-*'))
assert (len(foos) == 5)
assert (len(bars) == 5)
def ufo_fbp(tomo, center, recon, theta, **kwargs):
"""
Reconstruct object using UFO's FBP pipeline
"""
import gi
gi.require_version('Ufo', '0.0')
from gi.repository import Ufo
width = tomo.shape[2]
theta = theta[1] - theta[0]
center = center[0]
g = Ufo.TaskGraph()
pm = Ufo.PluginManager()
sched = Ufo.Scheduler()
input_task = Ufo.InputTask()
output_task = Ufo.OutputTask()
fft = pm.get_task('fft')
ifft = pm.get_task('ifft')
fltr = pm.get_task('filter')
backproject = pm.get_task('backproject')
ifft.set_properties(crop_width=width)
backproject.set_properties(axis_pos=center,
angle_step=theta,
angle_offset=np.pi)
g.connect_nodes(input_task, fft)
g.connect_nodes(fft, fltr)
g.connect_nodes(fltr, ifft)
g.connect_nodes(ifft, backproject)
g.connect_nodes(backproject, output_task)
args = (input_task, output_task, tomo, recon)
thread = threading.Thread(target=_process_data, args=args)
thread.start()
sched.run(g)
thread.join()
logger.info("UFO+FBP run time: {}s".format(sched.props.time))
def __init__(self, axis_pos=None, flat_row=None, dark_row=None):
self.pm = PluginManager()
self.sino_correction = self.pm.get_task('flat-field-correction')
self.fft = self.pm.get_task('fft', dimensions=1)
self.ifft = self.pm.get_task('ifft', dimensions=1)
self.fltr = self.pm.get_task('filter')
self.backprojector = self.pm.get_task('backproject')
if axis_pos:
self.backprojector.props.axis_pos = axis_pos
self.sino_correction.props.sinogram_input = True
graph = Ufo.TaskGraph()
graph.connect_nodes(self.sino_correction, self.fft)
graph.connect_nodes(self.fft, self.fltr)
graph.connect_nodes(self.fltr, self.ifft)
graph.connect_nodes(self.ifft, self.backprojector)
super(FlatCorrectedBackproject, self).__init__(graph, get_output=True)
self.flat_row = flat_row
self.dark_row = dark_row
def tomo(params):
# Create reader and writer
if params.projections and params.sinograms:
raise RuntimeError("Cannot specify both --projections and --sinograms.")
if params.projections is None and params.sinograms is None:
reader, width, height = get_dummy_reader(params)
else:
if params.projections:
reader, width, height = get_projection_reader(params)
else:
reader, width, height = get_sinogram_reader(params)
axis = params.axis or width / 2.0
if params.projections and params.resize:
width /= params.resize
height /= params.resize
axis /= params.resize
LOG.debug("Input dimensions: {}x{} pixels".format(width, height))
writer = get_writer(pm, params)
# Setup graph depending on the chosen method and input data
g = Ufo.TaskGraph()
if params.projections is not None:
if params.number:
count = len(range(params.start, params.start + params.number, params.step))
else:
count = len(get_filenames(params.projections))
LOG.debug("Number of projections: {}".format(count))
sino_output = get_task('transpose-projections', number=count)
if params.darks and params.flats:
g.connect_nodes(create_flat_correct_pipeline(params, g), sino_output)
else:
g.connect_nodes(reader, sino_output)
if height:
# Sinogram height is the one needed for further padding
height = count
else:
sino_output = reader
if params.method == 'fbp':
fft = get_task('fft', dimensions=1)
ifft = get_task('ifft', dimensions=1)
fltr = get_task('filter', filter=params.projection_filter)
bp = get_task('backproject', axis_pos=axis)
if params.angle:
bp.props.angle_step = params.angle
if params.offset:
bp.props.angle_offset = params.offset
if width and height:
# Pad the image with its extent to prevent reconstuction ring
pad = get_task('pad')
crop = get_task('crop')
setup_padding(pad, crop, width, height, params.projection_padding_mode)
LOG.debug("Padding input to: {}x{} pixels".format(pad.props.width, pad.props.height))
g.connect_nodes(sino_output, pad)
g.connect_nodes(pad, fft)
g.connect_nodes(fft, fltr)
g.connect_nodes(fltr, ifft)
g.connect_nodes(ifft, crop)
g.connect_nodes(crop, bp)
else:
if params.crop_width:
ifft.props.crop_width = int(params.crop_width)
LOG.debug("Cropping to {} pixels".format(ifft.props.crop_width))
g.connect_nodes(sino_output, fft)
g.connect_nodes(fft, fltr)
g.connect_nodes(fltr, ifft)
g.connect_nodes(ifft, bp)
g.connect_nodes(bp, writer)
if params.method in ('sart', 'sirt', 'sbtv', 'asdpocs'):
projector = pm.get_task_from_package('ir', 'parallel-projector')
projector.set_properties(model='joseph', is_forward=False)
projector.set_properties(axis_position=axis)
projector.set_properties(step=params.angle if params.angle else np.pi / 180.0)
method = pm.get_task_from_package('ir', params.method)
method.set_properties(projector=projector, num_iterations=params.num_iterations)
if params.method in ('sart', 'sirt'):
method.set_properties(relaxation_factor=params.relaxation_factor)
if params.method == 'asdpocs':
minimizer = pm.get_task_from_package('ir', 'sirt')
method.set_properties(df_minimizer=minimizer)
if params.method == 'sbtv':
# FIXME: the lambda keyword is preventing from the following
# assignment ...
# method.props.lambda = params.lambda
method.set_properties(mu=params.mu)
g.connect_nodes(sino_output, method)
g.connect_nodes(method, writer)
if params.method == 'dfi':
oversampling = params.oversampling or 1
pad = get_task('zeropad', center_of_rotation=axis, oversampling=oversampling)
fft = get_task('fft', dimensions=1, auto_zeropadding=0)
dfi = get_task('dfi-sinc')
ifft = get_task('ifft', dimensions=2)
swap_forward = get_task('swap-quadrants')
swap_backward = get_task('swap-quadrants')
if params.angle:
dfi.props.angle_step = params.angle
g.connect_nodes(sino_output, pad)
g.connect_nodes(pad, fft)
g.connect_nodes(fft, dfi)
g.connect_nodes(dfi, swap_forward)
g.connect_nodes(swap_forward, ifft)
g.connect_nodes(ifft, swap_backward)
if width:
crop = get_task('crop')
crop.set_properties(from_center=True, width=width, height=width)
g.connect_nodes(swap_backward, crop)
g.connect_nodes(crop, writer)
else:
g.connect_nodes(swap_backward, writer)
scheduler = Ufo.Scheduler()
if hasattr(scheduler.props, 'enable_tracing'):
LOG.debug("Use tracing: {}".format(params.enable_tracing))
scheduler.props.enable_tracing = params.enable_tracing
scheduler.run(g)
duration = scheduler.props.time
LOG.info("Execution time: {} s".format(duration))
return duration
def reset(self):
self.graph = Ufo.TaskGraph()
def __init__(self, args, resources=None, gpu_index=0, do_normalization=False,
region=None, copy_inputs=False):
if args.width is None or args.height is None:
raise GeneralBackprojectError('width and height must be set in GeneralBackprojectArgs')
scheduler = Ufo.FixedScheduler()
if resources:
scheduler.set_resources(resources)
gpu = scheduler.get_resources().get_gpu_nodes()[gpu_index]
self.args = copy.deepcopy(args)
x_region, y_region, z_region = get_reconstruction_regions(self.args, store=True,
dtype=float)
set_projection_filter_scale(self.args)
if region is not None:
self.args.region = region
LOG.debug('Creating reconstructor for gpu %d, region: %s', gpu_index, self.args.region)
geometry = CTGeometry(self.args)
if not self.args.disable_projection_crop:
geometry.optimize_args()
self.args = geometry.args
regions = make_runs([gpu], [gpu_index], x_region, y_region, self.args.region,
DTYPE_CL_SIZE[self.args.store_type],
slices_per_device=self.args.slices_per_device,
slice_memory_coeff=self.args.slice_memory_coeff,
data_splitting_policy=self.args.data_splitting_policy)
if len(regions) > 1:
raise GeneralBackprojectError('Region does not fit to the GPU memory')
graph = Ufo.TaskGraph()
# Normalization
self.ffc = None
self.do_normalization = do_normalization
if do_normalization:
self.ffc = get_task('flat-field-correct', processing_node=gpu)
self.ffc.props.fix_nan_and_inf = self.args.fix_nan_and_inf
self.ffc.props.absorption_correct = self.args.absorptivity
self._darks_averaged = False
self._flats_averaged = False
self.dark_avg = get_task('average', processing_node=gpu)
self.flat_avg = get_task('average', processing_node=gpu)
graph.connect_nodes_full(self.dark_avg, self.ffc, 1)
graph.connect_nodes_full(self.flat_avg, self.ffc, 2)
(first, last) = setup_graph(self.args, graph, x_region, y_region, self.args.region,
source=self.ffc, gpu=gpu, index=gpu_index, do_output=False,
make_reader=False)
output_dims = 2
if args.slice_metric:
output_dims = 1
metric = self.args.slice_metric
if args.slice_metric == 'sag':
metric = 'sum'
gradient_task = get_task('gradient', processing_node=gpu, direction='both_abs')
graph.connect_nodes(last, gradient_task)
last = gradient_task
measure_task = get_task('measure', processing_node=gpu, axis=-1, metric=metric)
graph.connect_nodes(last, measure_task)
elif first == last:
# There are no other processing steps other than back projection
LOG.debug('Only back projection, no other processing')
graph = first
super().__init__(graph, get_output=True, output_dims=output_dims, scheduler=scheduler,
copy_inputs=copy_inputs)
if self.do_normalization:
# Setup input tasks for normalization images averaging. Our parent picks up only the two
# averagers and not the ffc's zero port for projections.
self.input_tasks[self.ffc] = [Ufo.InputTask()]
self.ufo_buffers[self.ffc] = [None]
self.graph.connect_nodes_full(self.input_tasks[self.ffc][0], self.ffc, 0)