def run_bdaooss_experiment(**kwargs):
"""This method runs the chain with a BDAoOSSShape hypothesis and given parameters.
This method is intended to be used in an Experiment instance. This method prepares the necessary data and
calls `run_chain`.
Parameters:
kwargs (dict): Keyword arguments are as follows
input_file (str): mame of the data file containing the observed image
data_folder (str): folder containing the data files
results_folder (str):
sampler (str): see `run_chain` function
inverted (bool): whether the input image is inverted, i.e., upside down
max_depth (int): maximum depth of the hypothesis trees
ll_variance (float): variance of the Gaussian likelihood
max_pixel_value (float): maximum pixel intensity value
change_size_variance (float): variance for the change part size move
change_viewpoint_variance (float): variance for the change viewpoint move
burn_in (int): see `run_chain` function
sample_count (int): see `run_chain` function
best_sample_count (int): see `run_chain` function
thinning_period (int): see `run_chain` function
report_period (int): see `run_chain` function
temperatures (list): see `run_chain` function
Returns:
dict: run results
"""
try:
input_file = kwargs['input_file']
results_folder = kwargs['results_folder']
data_folder = kwargs['data_folder']
sampler = kwargs['sampler']
inverted = kwargs['inverted']
ll_variance = kwargs['ll_variance']
max_pixel_value = kwargs['max_pixel_value']
max_depth = None
if 'max_depth' in kwargs:
max_depth = kwargs['max_depth']
change_size_variance = kwargs['change_size_variance']
change_viewpoint_variance = kwargs['change_viewpoint_variance']
burn_in = kwargs['burn_in']
sample_count = kwargs['sample_count']
best_sample_count = kwargs['best_sample_count']
thinning_period = kwargs['thinning_period']
report_period = kwargs['report_period']
temperatures = None
if 'temperatures' in kwargs:
temperatures = kwargs['temperatures']
except KeyError as e:
raise ValueError("All experiment parameters should be provided. Missing parameter {0:s}".format(e.message))
import numpy as np
import mcmclib.proposal as proposal
import i3d_proposal
import vision_forward_model as vfm
# read the data file
if not inverted:
viewpoint = [[np.sqrt(8.0), -45.0, 45.0]]
data = np.load("{0:s}/{1:s}_single_view.npy".format(data_folder, input_file))
custom_lighting = True
else:
data = np.load("{0:s}/{1:s}_single_view_inverted.npy".format(data_folder, input_file))
viewpoint = [[np.sqrt(8.0), -45.0, 135.0]]
# for inverted images we use VTK's default lighting because in the custom lighting, bottoms of objects are not
# well illuminated
custom_lighting = False
render_size = data.shape[1:]
fwm = vfm.VisionForwardModel(render_size=render_size, custom_lighting=custom_lighting, offscreen_rendering=True)
shape_params = {'LL_VARIANCE': ll_variance, 'MAX_PIXEL_VALUE': max_pixel_value}
kernel_params = {'CHANGE_SIZE_VARIANCE': change_size_variance,
'CHANGE_VIEWPOINT_VARIANCE': change_viewpoint_variance}
import bdaooss_shape as bdaooss
moves = {'change_viewpoint': i3d_proposal.change_viewpoint_z,
'bdaooss_add_remove_part': bdaooss.bdaooss_add_remove_part,
'bdaooss_change_part_size_local': bdaooss.bdaooss_change_part_size_local,
'bdaooss_change_part_dock_face': bdaooss.bdaooss_change_part_dock_face}
if max_depth is None:
hypothesis_class = 'BDAoOSShape'
h = bdaooss.BDAoOSSShape(forward_model=fwm, viewpoint=viewpoint, params=shape_params)
else:
import bdaooss_shape_maxd as bdaooss_maxd
hypothesis_class = 'BDAoOSSShapeMaxD'
h = bdaooss_maxd.BDAoOSSShapeMaxD(forward_model=fwm, max_depth=max_depth, viewpoint=viewpoint,
params=shape_params)
kernel_params['MAX_DEPTH'] = max_depth
# form the proposal
kernel = proposal.RandomMixtureProposal(moves=moves, params=kernel_params)
results = run_chain(name=input_file, sampler=sampler, initial_h=h, data=data, kernel=kernel, burn_in=burn_in,
thinning_period=thinning_period, sample_count=sample_count, best_sample_count=best_sample_count,
report_period=report_period,
results_folder="{0:s}/{1:s}".format(results_folder, hypothesis_class),
temperatures=temperatures)
return results
def run_voxel_based_shape_experiment(**kwargs):
"""This method runs the chain with a VoxelBasedShape hypothesis and given parameters.
This method is intended to be used in an Experiment instance. This method prepares the necessary data and
calls `run_chain`.
Parameters:
kwargs (dict): Keyword arguments are as follows
input_file (str): mame of the data file containing the observed image
data_folder (str): folder containing the data files
results_folder (str):
sampler (str): see `run_chain` function
max_depth (int): maximum depth of hypothesis
ll_variance (float): variance of the Gaussian likelihood
max_pixel_value (float): maximum pixel intensity value
change_viewpoint_variance (float): variance for the change viewpoint move
scale_space_variance (float): variance for the scale space move
burn_in (int): see `run_chain` function
sample_count (int): see `run_chain` function
best_sample_count (int): see `run_chain` function
thinning_period (int): see `run_chain` function
report_period (int): see `run_chain` function
temperatures (list): see `run_chain` function
Returns:
dict: run results
"""
try:
input_file = kwargs['input_file']
results_folder = kwargs['results_folder']
data_folder = kwargs['data_folder']
sampler = kwargs['sampler']
max_depth = None
if 'max_depth' in kwargs:
max_depth = kwargs['max_depth']
ll_variance = kwargs['ll_variance']
max_pixel_value = kwargs['max_pixel_value']
change_viewpoint_variance = kwargs['change_viewpoint_variance']
scale_space_variance = kwargs['scale_space_variance']
burn_in = kwargs['burn_in']
sample_count = kwargs['sample_count']
best_sample_count = kwargs['best_sample_count']
thinning_period = kwargs['thinning_period']
report_period = kwargs['report_period']
temperatures = None
if 'temperatures' in kwargs:
temperatures = kwargs['temperatures']
except KeyError as e:
raise ValueError("All experiment parameters should be provided. Missing parameter {0:s}".format(e.message))
import numpy as np
import mcmclib.proposal as proposal
import i3d_proposal
import vision_forward_model as vfm
# read the data file
data = np.load("{0:s}/{1:s}_single_view.npy".format(data_folder, input_file))
render_size = data.shape[1:]
fwm = vfm.VisionForwardModel(render_size=render_size)
shape_params = {'LL_VARIANCE': ll_variance, 'MAX_PIXEL_VALUE': max_pixel_value}
kernel_params = {'CHANGE_VIEWPOINT_VARIANCE': change_viewpoint_variance,
'SCALE_SPACE_VARIANCE': scale_space_variance}
import voxel_based_shape as vox
moves = {'change_viewpoint': i3d_proposal.change_viewpoint_z,
'voxel_flip_full_vs_empty': vox.voxel_based_shape_flip_full_vs_empty,
'voxel_flip_partial_vs_full': vox.voxel_based_shape_flip_full_vs_partial,
'voxel_flip_partial_vs_empty': vox.voxel_based_shape_flip_empty_vs_partial,
'voxel_scale_space': vox.voxel_scale_space}
viewpoint = [(np.sqrt(8.0), -45.0, 45.0)]
hypothesis_class = 'VoxelBasedShape'
if max_depth is None:
h = vox.VoxelBasedShape(forward_model=fwm, viewpoint=viewpoint, params=shape_params)
else:
hypothesis_class = 'VoxelBasedShapeMaxD'
h = vox.VoxelBasedShapeMaxD(forward_model=fwm, viewpoint=viewpoint, params=shape_params, max_depth=max_depth)
kernel_params['MAX_DEPTH'] = max_depth
# form the proposal
kernel = proposal.RandomMixtureProposal(moves=moves, params=kernel_params)
results = run_chain(name=input_file, sampler=sampler, initial_h=h, data=data, kernel=kernel, burn_in=burn_in,
thinning_period=thinning_period, sample_count=sample_count, best_sample_count=best_sample_count,
report_period=report_period,
results_folder="{0:s}/{1:s}".format(results_folder, hypothesis_class),
temperatures=temperatures)
return results
def run_bdaooss_experiment(**kwargs):
"""This method runs the chain with a BDAoOSSShape hypothesis and given parameters.
This method is intended to be used in an Experiment instance. This method prepares the necessary data and
calls `run_chain`.
Parameters:
kwargs (dict): Keyword arguments are as follows
input_file (str): mame of the data file containing the observed image
data_folder (str): folder containing the data files
results_folder (str):
sampler (str): see `run_chain` function
inverted (bool): whether the input image is inverted, i.e., upside down
max_depth (int): maximum depth of the hypothesis trees
ll_variance (float): variance of the Gaussian likelihood
max_pixel_value (float): maximum pixel intensity value
change_size_variance (float): variance for the change part size move
change_viewpoint_variance (float): variance for the change viewpoint move
burn_in (int): see `run_chain` function
sample_count (int): see `run_chain` function
best_sample_count (int): see `run_chain` function
thinning_period (int): see `run_chain` function
report_period (int): see `run_chain` function
temperatures (list): see `run_chain` function
Returns:
dict: run results
"""
try:
input_file = kwargs['input_file']
results_folder = kwargs['results_folder']
data_folder = kwargs['data_folder']
sampler = kwargs['sampler']
inverted = kwargs['inverted']
ll_variance = kwargs['ll_variance']
max_pixel_value = kwargs['max_pixel_value']
max_depth = None
if 'max_depth' in kwargs:
max_depth = kwargs['max_depth']
change_size_variance = kwargs['change_size_variance']
change_viewpoint_variance = kwargs['change_viewpoint_variance']
burn_in = kwargs['burn_in']
sample_count = kwargs['sample_count']
best_sample_count = kwargs['best_sample_count']
thinning_period = kwargs['thinning_period']
report_period = kwargs['report_period']
temperatures = None
if 'temperatures' in kwargs:
temperatures = kwargs['temperatures']
except KeyError as e:
raise ValueError(
"All experiment parameters should be provided. Missing parameter {0:s}"
.format(e.message))
import numpy as np
import mcmclib.proposal as proposal
import i3d_proposal
import vision_forward_model as vfm
# read the data file
if not inverted:
viewpoint = [[np.sqrt(8.0), -45.0, 45.0]]
data = np.load("{0:s}/{1:s}_single_view.npy".format(
data_folder, input_file))
custom_lighting = True
else:
data = np.load("{0:s}/{1:s}_single_view_inverted.npy".format(
data_folder, input_file))
viewpoint = [[np.sqrt(8.0), -45.0, 135.0]]
# for inverted images we use VTK's default lighting because in the custom lighting, bottoms of objects are not
# well illuminated
custom_lighting = False
render_size = data.shape[1:]
fwm = vfm.VisionForwardModel(render_size=render_size,
custom_lighting=custom_lighting,
offscreen_rendering=True)
shape_params = {
'LL_VARIANCE': ll_variance,
'MAX_PIXEL_VALUE': max_pixel_value
}
kernel_params = {
'CHANGE_SIZE_VARIANCE': change_size_variance,
'CHANGE_VIEWPOINT_VARIANCE': change_viewpoint_variance
}
import bdaooss_shape as bdaooss
moves = {
'change_viewpoint': i3d_proposal.change_viewpoint_z,
'bdaooss_add_remove_part': bdaooss.bdaooss_add_remove_part,
'bdaooss_change_part_size_local':
bdaooss.bdaooss_change_part_size_local,
'bdaooss_change_part_dock_face': bdaooss.bdaooss_change_part_dock_face
}
if max_depth is None:
hypothesis_class = 'BDAoOSShape'
h = bdaooss.BDAoOSSShape(forward_model=fwm,
viewpoint=viewpoint,
params=shape_params)
else:
import bdaooss_shape_maxd as bdaooss_maxd
hypothesis_class = 'BDAoOSSShapeMaxD'
h = bdaooss_maxd.BDAoOSSShapeMaxD(forward_model=fwm,
max_depth=max_depth,
viewpoint=viewpoint,
params=shape_params)
kernel_params['MAX_DEPTH'] = max_depth
# form the proposal
kernel = proposal.RandomMixtureProposal(moves=moves, params=kernel_params)
results = run_chain(name=input_file,
sampler=sampler,
initial_h=h,
data=data,
kernel=kernel,
burn_in=burn_in,
thinning_period=thinning_period,
sample_count=sample_count,
best_sample_count=best_sample_count,
report_period=report_period,
results_folder="{0:s}/{1:s}".format(
results_folder, hypothesis_class),
temperatures=temperatures)
return results
