def getprobability(object, grasps):
obj_name = object[1]
sdf_name = object[2]
obj_mesh = of.ObjFile(obj_name).read()
sdf_ = sf.SdfFile(sdf_name).read()
obj = go.GraspableObject3D(sdf_,
mesh=obj_mesh,
key=object[0].replace("_features.txt", ""),
model_name=obj_name)
config_name = "cfg/correlated.yaml"
config = ec.ExperimentConfig(config_name)
np.random.seed(100)
brute_force_iter = config['bandit_brute_force_iter']
max_iter = config['bandit_max_iter']
confidence = config['bandit_confidence']
snapshot_rate = config['bandit_snapshot_rate']
tc_list = [
tc.MaxIterTerminationCondition(max_iter),
# tc.ConfidenceTerminationCondition(confidence)
]
# run bandits!
graspable_rv = pfc.GraspableObjectGaussianPose(obj, config)
f_rv = scipy.stats.norm(config['friction_coef'],
config['sigma_mu']) # friction Gaussian RV
# compute feature vectors for all grasps
feature_extractor = ff.GraspableFeatureExtractor(obj, config)
all_features = feature_extractor.compute_all_features(grasps)
candidates = []
for grasp, features in zip(grasps, all_features):
grasp_rv = pfc.ParallelJawGraspGaussian(grasp, config)
pfc_rv = pfc.ForceClosureRV(grasp_rv, graspable_rv, f_rv, config)
if features is None:
pass
else:
pfc_rv.set_features(features)
candidates.append(pfc_rv)
def phi(rv):
return rv.features
nn = kernels.KDTree(phi=phi)
kernel = kernels.SquaredExponentialKernel(sigma=config['kernel_sigma'],
l=config['kernel_l'],
phi=phi)
objective = objectives.RandomBinaryObjective()
# uniform allocation for true values
ua = das.UniformAllocationMean(objective, candidates)
ua_result = ua.solve(
termination_condition=tc.MaxIterTerminationCondition(brute_force_iter),
snapshot_rate=snapshot_rate)
estimated_pfc = models.BetaBernoulliModel.beta_mean(
ua_result.models[-1].alphas, ua_result.models[-1].betas)
return estimated_pfc
def run_ua_on(obj, config):
# sample grasps
sample_start = time.clock()
if config['grasp_sampler'] == 'antipodal':
logging.info('Using antipodal grasp sampling')
sampler = ags.AntipodalGraspSampler(config)
grasps = sampler.generate_grasps(
obj, check_collisions=config['check_collisions'])
# pad with gaussian grasps
num_grasps = len(grasps)
min_num_grasps = config['min_num_grasps']
if num_grasps < min_num_grasps:
target_num_grasps = min_num_grasps - num_grasps
gaussian_sampler = gs.GaussianGraspSampler(config)
gaussian_grasps = gaussian_sampler.generate_grasps(
obj,
target_num_grasps=target_num_grasps,
check_collisions=config['check_collisions'])
grasps.extend(gaussian_grasps)
else:
logging.info('Using Gaussian grasp sampling')
sampler = gs.GaussianGraspSampler(config)
grasps = sampler.generate_grasps(
obj, check_collisions=config['check_collisions'])
# generate pfc candidates
graspable_rv = pfc.GraspableObjectGaussianPose(obj, config)
f_rv = scipy.stats.norm(config['friction_coef'], config['sigma_mu'])
candidates = []
for grasp in grasps:
logging.info('Adding grasp %d candidate' % (len(candidates)))
grasp_rv = pfc.ParallelJawGraspGaussian(grasp, config)
pfc_rv = pfc.ForceClosureRV(grasp_rv, graspable_rv, f_rv, config)
candidates.append(pfc_rv)
logging.info('%d candidates', len(candidates))
brute_force_iter = config['bandit_brute_force_iter'] * len(candidates)
snapshot_rate = brute_force_iter
objective = objectives.RandomBinaryObjective()
ua = das.UniformAllocationMean(objective, candidates)
logging.info('Running uniform allocation for true pfc.')
bandit_start = time.clock()
ua_result = ua.solve(
termination_condition=tc.MaxIterTerminationCondition(brute_force_iter),
snapshot_rate=snapshot_rate)
bandit_end = time.clock()
bandit_duration = bandit_end - bandit_start
logging.info('Uniform allocation (%d iters) took %f sec' %
(brute_force_iter, bandit_duration))
return ua_result
def label_correlated(obj, chunk, dest, config, plot=False, load=True):
"""Label an object with grasps according to probability of force closure,
using correlated bandits."""
bandit_start = time.clock()
#np.random.seed(100)
# sample grasps
sample_start = time.clock()
if config['grasp_sampler'] == 'antipodal':
logging.info('Using antipodal grasp sampling')
sampler = ags.AntipodalGraspSampler(config)
grasps = sampler.generate_grasps(obj, check_collisions=config['check_collisions'], vis=False)
# pad with gaussian grasps
num_grasps = len(grasps)
min_num_grasps = config['min_num_grasps']
if num_grasps < min_num_grasps:
target_num_grasps = min_num_grasps - num_grasps
gaussian_sampler = gs.GaussianGraspSampler(config)
gaussian_grasps = gaussian_sampler.generate_grasps(obj, target_num_grasps=target_num_grasps,
check_collisions=config['check_collisions'], vis=plot)
grasps.extend(gaussian_grasps)
else:
logging.info('Using Gaussian grasp sampling')
sampler = gs.GaussianGraspSampler(config)
grasps = sampler.generate_grasps(obj, check_collisions=config['check_collisions'], vis=plot,
grasp_gen_mult = 6)
sample_end = time.clock()
sample_duration = sample_end - sample_start
logging.info('Loaded %d grasps' %(len(grasps)))
logging.info('Grasp candidate loading took %f sec' %(sample_duration))
if not grasps:
logging.info('Skipping %s' %(obj.key))
return None
# extract load features for all grasps
feature_start = time.clock()
feature_extractor = ff.GraspableFeatureExtractor(obj, config)
all_features = feature_extractor.compute_all_features(grasps)
feature_end = time.clock()
feature_duration = feature_end - feature_start
logging.info('Loaded %d features' %(len(all_features)))
logging.info('Grasp feature loading took %f sec' %(feature_duration))
# bandit params
num_trials = config['num_trials']
brute_force_iter = config['bandit_brute_force_iter']
max_iter = config['bandit_max_iter']
confidence = config['bandit_confidence']
snapshot_rate = config['bandit_snapshot_rate']
brute_snapshot_rate = config['bandit_brute_force_snapshot_rate']
tc_list = [
tc.MaxIterTerminationCondition(max_iter),
]
# set up randome variables
graspable_rv = pfc.GraspableObjectGaussianPose(obj, config)
f_rv = scipy.stats.norm(config['friction_coef'], config['sigma_mu']) # friction Gaussian RV
candidates = []
for grasp, features in zip(grasps, all_features):
logging.info('Adding grasp %d' %len(candidates))
grasp_rv = pfc.ParallelJawGraspGaussian(grasp, config)
pfc_rv = pfc.ForceClosureRV(grasp_rv, graspable_rv, f_rv, config)
if features is None:
logging.info('Could not compute features for grasp.')
else:
pfc_rv.set_features(features)
candidates.append(pfc_rv)
# feature transform
def phi(rv):
return rv.features
# create nn structs for kernels
nn = kernels.KDTree(phi=phi)
kernel = kernels.SquaredExponentialKernel(
sigma=config['kernel_sigma'], l=config['kernel_l'], phi=phi)
objective = objectives.RandomBinaryObjective()
# uniform allocation for true values
ua_brute = das.UniformAllocationMean(objective, candidates)
logging.info('Running uniform allocation for true pfc.')
ua_brute_result = ua_brute.solve(termination_condition=tc.MaxIterTerminationCondition(brute_force_iter),
snapshot_rate=brute_snapshot_rate)
final_model = ua_brute_result.models[-1]
estimated_pfc = models.BetaBernoulliModel.beta_mean(final_model.alphas, final_model.betas)
save_grasps(grasps, estimated_pfc, obj, dest, num_successes=final_model.alphas, num_failures=final_model.betas)
# run bandits for several trials
ua_rewards = []
ts_rewards = []
ts_corr_rewards = []
for t in range(num_trials):
logging.info('Trial %d' %(t))
# Uniform sampling
ua = das.UniformAllocationMean(objective, candidates)
logging.info('Running Uniform allocation.')
ua_result = ua.solve(termination_condition=tc.OrTerminationCondition(tc_list), snapshot_rate=snapshot_rate)
# Thompson sampling
ts = das.ThompsonSampling(objective, candidates)
logging.info('Running Thompson sampling.')
ts_result = ts.solve(termination_condition=tc.OrTerminationCondition(tc_list), snapshot_rate=snapshot_rate)
# correlated Thompson sampling for even faster convergence
ts_corr = das.CorrelatedThompsonSampling(
objective, candidates, nn, kernel, tolerance=config['kernel_tolerance'])
logging.info('Running correlated Thompson sampling.')
ts_corr_result = ts_corr.solve(termination_condition=tc.OrTerminationCondition(tc_list), snapshot_rate=snapshot_rate)
# compile results
ua_normalized_reward = reward_vs_iters(ua_result, estimated_pfc)
ts_normalized_reward = reward_vs_iters(ts_result, estimated_pfc)
ts_corr_normalized_reward = reward_vs_iters(ts_corr_result, estimated_pfc)
ua_rewards.append(ua_normalized_reward)
ts_rewards.append(ts_normalized_reward)
ts_corr_rewards.append(ts_corr_normalized_reward)
# get the bandit rewards
all_ua_rewards = np.array(ua_rewards)
all_ts_rewards = np.array(ts_rewards)
all_ts_corr_rewards = np.array(ts_corr_rewards)
# compute avg normalized rewards
avg_ua_rewards = np.mean(all_ua_rewards, axis=0)
avg_ts_rewards = np.mean(all_ts_rewards, axis=0)
avg_ts_corr_rewards = np.mean(all_ts_corr_rewards, axis=0)
# kernel matrix
kernel_matrix = kernel.matrix(candidates)
return BanditCorrelatedExperimentResult(avg_ua_rewards, avg_ts_rewards, avg_ts_corr_rewards,
estimated_pfc, ua_result.iters, kernel_matrix, obj_key=obj.key)
def test_window_correlation(width, num_steps, vis=True):
import scipy
import sdf_file, obj_file
import discrete_adaptive_samplers as das
import experiment_config as ec
import feature_functions as ff
import graspable_object as go # weird Python issues
import kernels
import models
import objectives
import pfc
import termination_conditions as tc
np.random.seed(100)
mesh_file_name = 'data/test/meshes/Co_clean.obj'
sdf_3d_file_name = 'data/test/sdf/Co_clean.sdf'
config = ec.ExperimentConfig('cfg/correlated.yaml')
config['window_width'] = width
config['window_steps'] = num_steps
brute_force_iter = 100
snapshot_rate = config['bandit_snapshot_rate']
sdf = sdf_file.SdfFile(sdf_3d_file_name).read()
mesh = obj_file.ObjFile(mesh_file_name).read()
graspable = go.GraspableObject3D(sdf, mesh)
grasp_axis = np.array([0, 1, 0])
grasp_width = 0.1
grasps = []
for z in [-0.030, -0.035, -0.040, -0.045]:
grasp_center = np.array([0, 0, z])
grasp = g.ParallelJawPtGrasp3D(
ParallelJawPtGrasp3D.configuration_from_params(
grasp_center, grasp_axis, grasp_width))
grasps.append(grasp)
graspable_rv = pfc.GraspableObjectGaussianPose(graspable, config)
f_rv = scipy.stats.norm(config['friction_coef'],
config['sigma_mu']) # friction Gaussian RV
# compute feature vectors for all grasps
feature_extractor = ff.GraspableFeatureExtractor(graspable, config)
all_features = feature_extractor.compute_all_features(grasps)
candidates = []
for grasp, features in zip(grasps, all_features):
logging.info('Adding grasp %d' % len(candidates))
grasp_rv = pfc.ParallelJawGraspGaussian(grasp, config)
pfc_rv = pfc.ForceClosureRV(grasp_rv, graspable_rv, f_rv, config)
pfc_rv.set_features(features)
candidates.append(pfc_rv)
if vis:
_, (c1, c2) = grasp.close_fingers(graspable)
plt.figure()
c1_proxy = c1.plot_friction_cone(color='m')
c2_proxy = c2.plot_friction_cone(color='y')
plt.legend([c1_proxy, c2_proxy], ['Cone 1', 'Cone 2'])
plt.title('Grasp %d' % (len(candidates)))
objective = objectives.RandomBinaryObjective()
ua = das.UniformAllocationMean(objective, candidates)
logging.info('Running uniform allocation for true pfc.')
ua_result = ua.solve(
termination_condition=tc.MaxIterTerminationCondition(brute_force_iter),
snapshot_rate=snapshot_rate)
estimated_pfc = models.BetaBernoulliModel.beta_mean(
ua_result.models[-1].alphas, ua_result.models[-1].betas)
print 'true pfc'
print estimated_pfc
def phi(rv):
return rv.features
kernel = kernels.SquaredExponentialKernel(sigma=config['kernel_sigma'],
l=config['kernel_l'],
phi=phi)
print 'kernel matrix'
print kernel.matrix(candidates)
if vis:
plt.show()
def label_correlated(obj,
chunk,
config,
plot=False,
priors_dataset=None,
nearest_features_names=None):
"""Label an object with grasps according to probability of force closure,
using correlated bandits."""
# bandit params
num_trials = config['num_trials']
max_iter = config['bandit_max_iter']
confidence = config['bandit_confidence']
snapshot_rate = config['bandit_snapshot_rate']
tc_list = [
tc.MaxIterTerminationCondition(max_iter),
]
bandit_start = time.clock()
np.random.seed(100)
candidates = load_candidate_grasps(obj, chunk)
if candidates is None:
return None
# feature transform
def phi(rv):
return rv.features
nn = kernels.KDTree(phi=phi)
kernel = kernels.SquaredExponentialKernel(sigma=config['kernel_sigma'],
l=config['kernel_l'],
phi=phi)
objective = objectives.RandomBinaryObjective()
# compute priors
logging.info('Computing priors')
if priors_dataset is None:
priors_dataset = chunk
prior_engine = pce.PriorComputationEngine(priors_dataset, config)
# Compute priors
all_alpha_priors = []
all_beta_priors = []
prior_comp_times = []
if nearest_features_names == None:
alpha_priors, beta_priors = prior_engine.compute_priors(
obj, candidates)
all_alpha_priors.append(alpha_priors)
all_beta_priors.append(beta_priors)
else:
for nearest_features_name in nearest_features_names:
logging.info('Computing priors using %s' % (nearest_features_name))
priors_start_time = time.time()
alpha_priors, beta_priors, neighbor_keys, neighbor_distances, neighbor_kernels, neighbor_pfc_diffs, num_grasp_neighbors = \
prior_engine.compute_priors(obj, candidates, nearest_features_name=nearest_features_name)
all_alpha_priors.append(alpha_priors)
all_beta_priors.append(beta_priors)
priors_end_time = time.time()
prior_comp_times.append(priors_end_time - priors_start_time)
logging.info(
'Priors for %s took %f' %
(nearest_features_name, priors_end_time - priors_start_time))
# pre-computed pfc values
logging.info('Computing regression errors')
true_pfc = np.array([c.grasp.quality for c in candidates])
prior_alphas = np.ones(true_pfc.shape)
prior_betas = np.ones(true_pfc.shape)
prior_pfc = 0.5 * np.ones(true_pfc.shape)
ce_loss = objectives.CrossEntropyLoss(true_pfc)
se_loss = objectives.SquaredErrorLoss(true_pfc)
we_loss = objectives.WeightedSquaredErrorLoss(true_pfc)
ccbp_ll = objectives.CCBPLogLikelihood(true_pfc)
ce_vals = [ce_loss(prior_pfc)]
se_vals = [se_loss(prior_pfc)]
we_vals = [se_loss(prior_pfc)] # uniform weights at first
ccbp_vals = [ccbp_ll.evaluate(prior_alphas, prior_betas)]
total_weights = [len(candidates)]
# compute estimated pfc values from alphas and betas
for alpha_prior, beta_prior in zip(all_alpha_priors, all_beta_priors):
estimated_pfc = models.BetaBernoulliModel.beta_mean(
np.array(alpha_prior), np.array(beta_prior))
estimated_vars = models.BetaBernoulliModel.beta_variance(
np.array(alpha_prior), np.array(beta_prior))
# compute losses
ce_vals.append(ce_loss(estimated_pfc))
se_vals.append(se_loss(estimated_pfc))
we_vals.append(we_loss.evaluate(estimated_pfc, estimated_vars))
ccbp_vals.append(
ccbp_ll.evaluate(np.array(alpha_prior), np.array(beta_prior)))
total_weights.append(np.sum(estimated_vars))
ce_vals = np.array(ce_vals)
se_vals = np.array(se_vals)
we_vals = np.array(we_vals)
ccbp_vals = np.array(ccbp_vals)
total_weights = np.array(total_weights)
# setup reward buffers
ua_rewards = []
ts_rewards = []
gi_rewards = []
ts_corr_rewards = []
bucb_corr_rewards = []
all_ts_corr_prior_rewards = []
for x in range(0, len(all_alpha_priors)):
all_ts_corr_prior_rewards.append([])
all_bucb_corr_prior_rewards = []
for x in range(0, len(all_alpha_priors)):
all_bucb_corr_prior_rewards.append([])
# setup runtime buffers
ua_runtimes = []
ts_runtimes = []
gi_runtimes = []
ts_corr_runtimes = []
bucb_corr_runtimes = []
all_ts_corr_prior_runtimes = []
for x in range(0, len(all_alpha_priors)):
all_ts_corr_prior_runtimes.append([])
all_bucb_corr_prior_runtimes = []
for x in range(0, len(all_alpha_priors)):
all_bucb_corr_prior_runtimes.append([])
# run bandits for several trials
logging.info('Running bandits')
for t in range(num_trials):
logging.info('Trial %d' % (t))
# Uniform sampling
ua = das.UniformAllocationMean(objective, candidates)
logging.info('Running Uniform allocation.')
ua_result = ua.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# Thompson sampling
ts = das.ThompsonSampling(objective, candidates)
logging.info('Running Thompson sampling.')
ts_result = ts.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# Gittins indices
gi = das.GittinsIndex98(objective, candidates)
logging.info('Running Gittins Indices.')
gi_result = gi.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# correlated Thompson sampling for even faster convergence
ts_corr = das.CorrelatedThompsonSampling(
objective,
candidates,
nn,
kernel,
tolerance=config['kernel_tolerance'],
p=config['lb_alpha'])
logging.info('Running correlated Thompson sampling.')
ts_corr_result = ts_corr.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# correlated Thompson sampling for even faster convergence
bucb_corr = das.CorrelatedGittins(
objective,
candidates,
nn,
kernel,
tolerance=config['kernel_tolerance'],
p=config['lb_alpha']) #horizon=max_iter)
logging.info('Running correlated Bayes UCB.')
bucb_corr_result = bucb_corr.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# correlated MAB for faster convergence
all_ts_corr_prior_ind = []
all_bucb_corr_prior_ind = []
for alpha_priors, beta_priors, ts_corr_prior_rewards, bucb_corr_prior_rewards, ts_corr_runtimes, bucb_corr_runtimes, nearest_features_name in \
zip(all_alpha_priors, all_beta_priors, all_ts_corr_prior_rewards, all_bucb_corr_prior_rewards, all_ts_corr_prior_runtimes, all_bucb_corr_prior_runtimes, nearest_features_names):
# thompson sampling
ts_corr_prior = das.CorrelatedThompsonSampling(
objective,
candidates,
nn,
kernel,
tolerance=config['kernel_tolerance'],
alpha_prior=alpha_priors,
beta_prior=beta_priors,
p=config['lb_alpha'])
logging.info(
'Running correlated Thompson sampling with priors from %s' %
(nearest_features_name))
ts_corr_prior_result = ts_corr_prior.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
ts_corr_prior_normalized_reward = reward_vs_iters(
ts_corr_prior_result, true_pfc)
ts_corr_prior_rewards.append(ts_corr_prior_normalized_reward)
ts_corr_runtimes.append(ts_corr_prior_result.total_time)
all_ts_corr_prior_ind.append(ts_corr_prior_result.best_pred_ind)
# bayes ucb
bucb_corr = das.CorrelatedGittins(
objective,
candidates,
nn,
kernel,
tolerance=config['kernel_tolerance'], #horizon=max_iter,
alpha_prior=alpha_priors,
beta_prior=beta_priors,
p=config['lb_alpha'])
logging.info('Running correlated Bayes UCB with priors from %s' %
(nearest_features_name))
bucb_corr_prior_result = bucb_corr.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
bucb_corr_prior_normalized_reward = reward_vs_iters(
bucb_corr_prior_result, true_pfc)
bucb_corr_prior_rewards.append(bucb_corr_prior_normalized_reward)
bucb_corr_runtimes.append(bucb_corr_prior_result.total_time)
all_bucb_corr_prior_ind.append(
bucb_corr_prior_result.best_pred_ind)
# compile results
ua_normalized_reward = reward_vs_iters(ua_result, true_pfc)
ts_normalized_reward = reward_vs_iters(ts_result, true_pfc)
gi_normalized_reward = reward_vs_iters(gi_result, true_pfc)
ts_corr_normalized_reward = reward_vs_iters(ts_corr_result, true_pfc)
bucb_corr_normalized_reward = reward_vs_iters(bucb_corr_result,
true_pfc)
ua_rewards.append(ua_normalized_reward)
ts_rewards.append(ts_normalized_reward)
gi_rewards.append(gi_normalized_reward)
ts_corr_rewards.append(ts_corr_normalized_reward)
bucb_corr_rewards.append(bucb_corr_normalized_reward)
ua_runtimes.append(ua_result.total_time)
ts_runtimes.append(ts_result.total_time)
gi_runtimes.append(gi_result.total_time)
ts_corr_runtimes.append(ts_corr_result.total_time)
bucb_corr_runtimes.append(bucb_corr_result.total_time)
if num_trials == 0:
return None
# get the bandit rewards
all_ua_rewards = np.array(ua_rewards)
all_ts_rewards = np.array(ts_rewards)
all_gi_rewards = np.array(gi_rewards)
all_ts_corr_rewards = np.array(ts_corr_rewards)
all_bucb_corr_rewards = np.array(bucb_corr_rewards)
all_avg_ts_corr_prior_rewards = []
for ts_corr_prior_rewards in all_ts_corr_prior_rewards:
all_avg_ts_corr_prior_rewards.append(
np.mean(np.array(ts_corr_prior_rewards), axis=0))
all_avg_bucb_corr_prior_rewards = []
for bucb_corr_prior_rewards in all_bucb_corr_prior_rewards:
all_avg_bucb_corr_prior_rewards.append(
np.mean(np.array(bucb_corr_prior_rewards), axis=0))
#all_avg_bucb_corr_prior_rewards.append([])
# get bandit indices
ua_ind = ua_result.best_pred_ind
ts_ind = ts_result.best_pred_ind
ts_corr_ind = ts_corr_result.best_pred_ind
bucb_corr_ind = bucb_corr_result.best_pred_ind
# compute avg normalized rewards
avg_ua_rewards = np.mean(all_ua_rewards, axis=0)
avg_ts_rewards = np.mean(all_ts_rewards, axis=0)
avg_gi_rewards = np.mean(all_gi_rewards, axis=0)
avg_ts_corr_rewards = np.mean(all_ts_corr_rewards, axis=0)
avg_bucb_corr_rewards = np.mean(all_bucb_corr_rewards, axis=0)
#avg_bucb_corr_rewards = all_bucb_corr_rewards
# compute avg runtimes
avg_ua_runtimes = np.mean(np.array(ua_runtimes), axis=0)
avg_ts_runtimes = np.mean(np.array(ts_runtimes), axis=0)
avg_ts_corr_runtimes = np.mean(np.array(ts_corr_runtimes), axis=0)
avg_bucb_corr_runtimes = np.mean(np.array(bucb_corr_runtimes), axis=0)
all_avg_ts_corr_prior_runtimes = []
for ts_corr_prior_runtimes in all_ts_corr_prior_runtimes:
all_avg_ts_corr_prior_runtimes.append(
np.mean(np.array(ts_corr_prior_runtimes), axis=0))
all_avg_bucb_corr_prior_runtimes = []
for bucb_corr_prior_runtimes in all_bucb_corr_prior_runtimes:
all_avg_bucb_corr_prior_runtimes.append(
np.mean(np.array(bucb_corr_prior_runtimes), axis=0))
# kernel matrix
kernel_matrix = kernel.matrix(candidates)
return BanditCorrelatedPriorExperimentResult(
avg_ua_rewards,
avg_ts_rewards,
avg_gi_rewards,
avg_ts_corr_rewards,
avg_bucb_corr_rewards,
all_avg_ts_corr_prior_rewards,
all_avg_bucb_corr_prior_rewards,
true_pfc,
ua_result.iters,
kernel_matrix, [], [], [],
ce_vals,
ccbp_vals,
we_vals,
len(candidates),
total_weights,
ua_ind,
ts_ind,
ts_corr_ind,
bucb_corr_ind,
all_ts_corr_prior_ind,
all_bucb_corr_prior_ind,
avg_ua_runtimes,
avg_ts_runtimes,
avg_ts_corr_runtimes,
avg_bucb_corr_runtimes,
all_avg_ts_corr_prior_runtimes,
all_avg_bucb_corr_prior_runtimes,
prior_comp_times,
obj_key=obj.key,
neighbor_keys=neighbor_keys)
def test_antipodal_grasp_thompson():
np.random.seed(100)
# h = plt.figure()
# ax = h.add_subplot(111, projection = '3d')
# load object
sdf_3d_file_name = 'data/test/sdf/Co_clean.sdf'
sf = sdf_file.SdfFile(sdf_3d_file_name)
sdf_3d = sf.read()
mesh_name = 'data/test/meshes/Co_clean.obj'
of = obj_file.ObjFile(mesh_name)
m = of.read()
graspable = go.GraspableObject3D(sdf_3d, mesh=m, model_name=mesh_name)
config = {
'grasp_width': 0.1,
'friction_coef': 0.5,
'num_cone_faces': 8,
'grasp_samples_per_surface_point': 4,
'dir_prior': 1.0,
'alpha_thresh_div': 32,
'rho_thresh': 0.75, # as pct of object max moment
'vis_antipodal': False,
'min_num_grasps': 20,
'alpha_inc': 1.1,
'rho_inc': 1.1,
'sigma_mu': 0.1,
'sigma_trans_grasp': 0.001,
'sigma_rot_grasp': 0.1,
'sigma_trans_obj': 0.001,
'sigma_rot_obj': 0.1,
'sigma_scale_obj': 0.1,
'num_prealloc_obj_samples': 100,
'num_prealloc_grasp_samples': 0,
'min_num_collision_free_grasps': 10,
'grasp_theta_res': 1
}
sampler = ags.AntipodalGraspSampler(config)
start_time = time.clock()
grasps, alpha_thresh, rho_thresh = sampler.generate_grasps(graspable,
vis=False)
end_time = time.clock()
duration = end_time - start_time
logging.info('Antipodal grasp candidate generation took %f sec' %
(duration))
# convert grasps to RVs for optimization
graspable_rv = GraspableObjectGaussianPose(graspable, config)
f_rv = scipy.stats.norm(config['friction_coef'], config['sigma_mu'])
candidates = []
for grasp in grasps:
grasp_rv = ParallelJawGraspGaussian(grasp, config)
candidates.append(ForceClosureRV(grasp_rv, graspable_rv, f_rv, config))
objective = objectives.RandomBinaryObjective()
# run bandits
eps = 5e-4
ua_tc_list = [tc.MaxIterTerminationCondition(1000)
] #, tc.ConfidenceTerminationCondition(eps)]
ua = das.UniformAllocationMean(objective, candidates)
ua_result = ua.solve(
termination_condition=tc.OrTerminationCondition(ua_tc_list),
snapshot_rate=100)
logging.info('Uniform allocation took %f sec' % (ua_result.total_time))
ts_tc_list = [
tc.MaxIterTerminationCondition(1000),
tc.ConfidenceTerminationCondition(eps)
]
ts = das.ThompsonSampling(objective, candidates)
ts_result = ts.solve(
termination_condition=tc.OrTerminationCondition(ts_tc_list),
snapshot_rate=100)
logging.info('Thompson sampling took %f sec' % (ts_result.total_time))
true_means = models.BetaBernoulliModel.beta_mean(
ua_result.models[-1].alphas, ua_result.models[-1].betas)
# plot results
plt.figure()
plot_value_vs_time_beta_bernoulli(ua_result, true_means, color='red')
plot_value_vs_time_beta_bernoulli(ts_result, true_means, color='blue')
plt.show()
das.plot_num_pulls_beta_bernoulli(ua_result)
plt.title('Observations Per Variable for Uniform allocation')
das.plot_num_pulls_beta_bernoulli(ts_result)
plt.title('Observations Per Variable for Thompson sampling')
plt.show()
def label_correlated(obj, chunk, dest, config, plot=False, load=True):
"""Label an object with grasps according to probability of force closure,
using correlated bandits."""
bandit_start = time.clock()
np.random.seed(100)
chunk = db.Chunk(config)
if not load:
# load grasps from database
sample_start = time.clock()
if config['grasp_sampler'] == 'antipodal':
logging.info('Using antipodal grasp sampling')
sampler = ags.AntipodalGraspSampler(config)
grasps = sampler.generate_grasps(
obj, check_collisions=config['check_collisions'], vis=plot)
# pad with gaussian grasps
num_grasps = len(grasps)
min_num_grasps = config['min_num_grasps']
if num_grasps < min_num_grasps:
target_num_grasps = min_num_grasps - num_grasps
gaussian_sampler = gs.GaussianGraspSampler(config)
gaussian_grasps = gaussian_sampler.generate_grasps(
obj,
target_num_grasps=target_num_grasps,
check_collisions=config['check_collisions'],
vis=plot)
grasps.extend(gaussian_grasps)
else:
logging.info('Using Gaussian grasp sampling')
sampler = gs.GaussianGraspSampler(config)
grasps = sampler.generate_grasps(
obj,
check_collisions=config['check_collisions'],
vis=plot,
grasp_gen_mult=6)
sample_end = time.clock()
sample_duration = sample_end - sample_start
logging.info('Loaded %d grasps' % (len(grasps)))
logging.info('Grasp candidate loading took %f sec' % (sample_duration))
if not grasps:
logging.info('Skipping %s' % (obj.key))
return None
else:
grasps = load_grasps(obj, dest)
grasps = grasps[:20]
# grasps = chunk.load_grasps(obj.key)
# load features for all grasps
feature_start = time.clock()
feature_extractor = ff.GraspableFeatureExtractor(obj, config)
features = feature_extractor.compute_all_features(grasps)
"""
if not load:
features = feature_extractor.compute_all_features(grasps)
else:
feature_loader = ff.GraspableFeatureLoader(obj, chunk.name, config)
features = feature_loader.load_all_features(grasps) # in same order as grasps
"""
feature_end = time.clock()
feature_duration = feature_end - feature_start
logging.info('Loaded %d features' % (len(features)))
logging.info('Grasp feature loading took %f sec' % (feature_duration))
# prune crappy grasps
all_features = []
all_grasps = []
for grasp, feature in zip(grasps, features):
if feature is not None:
all_grasps.append(grasp)
all_features.append(feature)
grasps = all_grasps
# compute distances for debugging
distances = np.zeros([len(grasps), len(grasps)])
i = 0
for feature_i in all_features:
j = 0
for feature_j in all_features:
distances[i, j] = np.linalg.norm(feature_i.phi - feature_j.phi)
j += 1
i += 1
# bandit params
brute_force_iter = config['bandit_brute_force_iter']
max_iter = config['bandit_max_iter']
confidence = config['bandit_confidence']
snapshot_rate = config['bandit_snapshot_rate']
tc_list = [
tc.MaxIterTerminationCondition(max_iter),
]
# run bandits!
graspable_rv = pfc.GraspableObjectGaussianPose(obj, config)
f_rv = scipy.stats.norm(config['friction_coef'],
config['sigma_mu']) # friction Gaussian RV
candidates = []
for grasp, features in zip(grasps, all_features):
logging.info('Adding grasp %d' % len(candidates))
grasp_rv = pfc.ParallelJawGraspGaussian(grasp, config)
pfc_rv = pfc.ForceClosureRV(grasp_rv, graspable_rv, f_rv, config)
if features is None:
logging.info('Could not compute features for grasp.')
else:
pfc_rv.set_features(features)
candidates.append(pfc_rv)
# feature transform
def phi(rv):
return rv.features
nn = kernels.KDTree(phi=phi)
kernel = kernels.SquaredExponentialKernel(sigma=config['kernel_sigma'],
l=config['kernel_l'],
phi=phi)
objective = objectives.RandomBinaryObjective()
if not load:
# uniform allocation for true values
ua = das.UniformAllocationMean(objective, candidates)
logging.info('Running uniform allocation for true pfc.')
ua_result = ua.solve(
termination_condition=tc.MaxIterTerminationCondition(
brute_force_iter),
snapshot_rate=snapshot_rate)
estimated_pfc = models.BetaBernoulliModel.beta_mean(
ua_result.models[-1].alphas, ua_result.models[-1].betas)
save_grasps(grasps, estimated_pfc, obj, dest)
# plot params
line_width = config['line_width']
font_size = config['font_size']
dpi = config['dpi']
# plot histograms
num_bins = 100
bin_edges = np.linspace(0, 1, num_bins + 1)
plt.figure()
n, bins, patches = plt.hist(estimated_pfc, bin_edges)
plt.xlabel('Probability of Success', fontsize=font_size)
plt.ylabel('Num Grasps', fontsize=font_size)
plt.title('Histogram of Grasps by Probability of Success',
fontsize=font_size)
plt.show()
exit(0)
else:
estimated_pfc = np.array([g.quality for g in grasps])
# debugging for examining bad features
bad_i = 0
bad_j = 1
grasp_i = grasps[bad_i]
grasp_j = grasps[bad_j]
pfc_i = estimated_pfc[bad_i]
pfc_j = estimated_pfc[bad_j]
features_i = all_features[bad_i]
features_j = all_features[bad_j]
feature_sq_diff = (features_i.phi - features_j.phi)**2
# grasp_i.close_fingers(obj, vis=True)
# grasp_j.close_fingers(obj, vis=True)
grasp_i.surface_information(obj, config['window_width'],
config['window_steps'])
grasp_j.surface_information(obj, config['window_width'],
config['window_steps'])
w = config['window_steps']
wi1 = np.reshape(features_i.extractors_[0].extractors_[1].phi, [w, w])
wi2 = np.reshape(features_i.extractors_[1].extractors_[1].phi, [w, w])
wj1 = np.reshape(features_j.extractors_[0].extractors_[1].phi, [w, w])
wj2 = np.reshape(features_j.extractors_[1].extractors_[1].phi, [w, w])
a = 0.1
plt.figure()
plt.subplot(2, 2, 1)
plt.imshow(wi1, cmap=plt.cm.Greys, interpolation='none')
plt.colorbar()
plt.clim(-a, a) # fixing color range for visual comparisons
plt.title('wi1')
plt.subplot(2, 2, 2)
plt.imshow(wi2, cmap=plt.cm.Greys, interpolation='none')
plt.colorbar()
plt.clim(-a, a) # fixing color range for visual comparisons
plt.title('wi2')
plt.subplot(2, 2, 3)
plt.imshow(wj1, cmap=plt.cm.Greys, interpolation='none')
plt.colorbar()
plt.clim(-a, a) # fixing color range for visual comparisons
plt.title('wj1')
plt.subplot(2, 2, 4)
plt.imshow(wj2, cmap=plt.cm.Greys, interpolation='none')
plt.colorbar()
plt.clim(-a, a) # fixing color range for visual comparisons
plt.title('wj2')
# plt.show()
# IPython.embed()
num_trials = config['num_trials']
ts_rewards = []
ts_corr_rewards = []
for t in range(num_trials):
logging.info('Trial %d' % (t))
# Thompson sampling
ts = das.ThompsonSampling(objective, candidates)
logging.info('Running Thompson sampling.')
ts_result = ts.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# correlated Thompson sampling for even faster convergence
ts_corr = das.CorrelatedThompsonSampling(
objective,
candidates,
nn,
kernel,
tolerance=config['kernel_tolerance'])
logging.info('Running correlated Thompson sampling.')
ts_corr_result = ts_corr.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
ts_normalized_reward = reward_vs_iters(ts_result, estimated_pfc)
ts_corr_normalized_reward = reward_vs_iters(ts_corr_result,
estimated_pfc)
ts_rewards.append(ts_normalized_reward)
ts_corr_rewards.append(ts_corr_normalized_reward)
# get the bandit rewards
all_ts_rewards = np.array(ts_rewards)
all_ts_corr_rewards = np.array(ts_corr_rewards)
avg_ts_rewards = np.mean(all_ts_rewards, axis=0)
avg_ts_corr_rewards = np.mean(all_ts_corr_rewards, axis=0)
# get correlations and plot
k = kernel.matrix(candidates)
k_vec = k.ravel()
pfc_arr = np.array([estimated_pfc]).T
pfc_diff = ssd.squareform(ssd.pdist(pfc_arr))
pfc_vec = pfc_diff.ravel()
bad_ind = np.where(pfc_diff > 1.0 - k)
plt.figure()
plt.scatter(k_vec, pfc_vec)
plt.xlabel('Kernel', fontsize=15)
plt.ylabel('PFC Diff', fontsize=15)
plt.title('Correlations', fontsize=15)
# plt.show()
# IPython.embed()
# plot params
line_width = config['line_width']
font_size = config['font_size']
dpi = config['dpi']
# plot histograms
num_bins = 100
bin_edges = np.linspace(0, 1, num_bins + 1)
plt.figure()
n, bins, patches = plt.hist(estimated_pfc, bin_edges)
plt.xlabel('Probability of Success', fontsize=font_size)
plt.ylabel('Num Grasps', fontsize=font_size)
plt.title('Histogram of Grasps by Probability of Success',
fontsize=font_size)
# plot the results
plt.figure()
plt.plot(ts_result.iters,
avg_ts_rewards,
c=u'g',
linewidth=line_width,
label='Thompson Sampling (Uncorrelated)')
plt.plot(ts_corr_result.iters,
avg_ts_corr_rewards,
c=u'r',
linewidth=line_width,
label='Thompson Sampling (Correlated)')
plt.xlim(0, np.max(ts_result.iters))
plt.ylim(0.5, 1)
plt.xlabel('Iteration', fontsize=font_size)
plt.ylabel('Normalized Probability of Force Closure', fontsize=font_size)
plt.title('Avg Normalized PFC vs Iteration', fontsize=font_size)
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(handles, labels, loc='lower right')
plt.show()
IPython.embed()
"""
# aggregate grasps
object_grasps = [candidates[i].grasp for i in ts_result.best_candidates]
grasp_qualities = list(ts_result.best_pred_means)
bandit_stop = time.clock()
logging.info('Bandits took %f sec' %(bandit_stop - bandit_start))
# get rotated, translated versions of grasps
delay = 0
pr2_grasps = []
pr2_grasp_qualities = []
theta_res = config['grasp_theta_res'] * np.pi
# grasp_checker = pgc.OpenRaveGraspChecker(view=config['vis_grasps'])
if config['vis_grasps']:
delay = config['vis_delay']
for grasp, grasp_quality in zip(object_grasps, grasp_qualities):
rotated_grasps = grasp.transform(obj.tf, theta_res)
# rotated_grasps = grasp_checker.prune_grasps_in_collision(obj, rotated_grasps, auto_step=True, close_fingers=False, delay=delay)
pr2_grasps.extend(rotated_grasps)
pr2_grasp_qualities.extend([grasp_quality] * len(rotated_grasps))
logging.info('Num grasps: %d' %(len(pr2_grasps)))
grasp_filename = os.path.join(dest, obj.key + '.json')
with open(grasp_filename, 'w') as f:
jsons.dump([g.to_json(quality=q) for g, q in
zip(pr2_grasps, pr2_grasp_qualities)], f)
ua_normalized_reward = reward_vs_iters(ua_result, estimated_pfc)
ts_normalized_reward = reward_vs_iters(ts_result, estimated_pfc)
ts_corr_normalized_reward = reward_vs_iters(ts_corr_result, estimated_pfc)
return BanditCorrelatedExperimentResult(ua_normalized_reward, ts_normalized_reward, ts_corr_normalized_reward,
ua_result, ts_result, ts_corr_result, obj_key=obj.key)
"""
return None
def label_correlated(obj, chunk, dest, config, plot=False):
"""Label an object with grasps according to probability of force closure,
using correlated bandits."""
bandit_start = time.clock()
np.random.seed(100)
# load grasps from database
sample_start = time.clock()
grasps = chunk.load_grasps(obj.key)
sample_end = time.clock()
sample_duration = sample_end - sample_start
logging.info('Loaded %d grasps' % (len(grasps)))
logging.info('Grasp candidate loading took %f sec' % (sample_duration))
if not grasps:
logging.info('Skipping %s' % (obj.key))
return None
# load features for all grasps
feature_start = time.clock()
feature_loader = ff.GraspableFeatureLoader(obj, chunk.name, config)
all_features = feature_loader.load_all_features(
grasps) # in same order as grasps
feature_end = time.clock()
feature_duration = feature_end - feature_start
logging.info('Loaded %d features' % (len(all_features)))
logging.info('Grasp feature loading took %f sec' % (feature_duration))
# bandit params
brute_force_iter = config['bandit_brute_force_iter']
max_iter = config['bandit_max_iter']
confidence = config['bandit_confidence']
snapshot_rate = config['bandit_snapshot_rate']
tc_list = [
tc.MaxIterTerminationCondition(max_iter),
# tc.ConfidenceTerminationCondition(confidence)
]
# run bandits!
graspable_rv = pfc.GraspableObjectGaussianPose(obj, config)
f_rv = scipy.stats.norm(config['friction_coef'],
config['sigma_mu']) # friction Gaussian RV
candidates = []
for grasp, features in zip(grasps, all_features):
logging.info('Adding grasp %d' % len(candidates))
grasp_rv = pfc.ParallelJawGraspGaussian(grasp, config)
pfc_rv = pfc.ForceClosureRV(grasp_rv, graspable_rv, f_rv, config)
if features is None:
logging.info('Could not compute features for grasp.')
else:
pfc_rv.set_features(features)
candidates.append(pfc_rv)
# feature transform
def phi(rv):
return rv.features
nn = kernels.KDTree(phi=phi)
kernel = kernels.SquaredExponentialKernel(sigma=config['kernel_sigma'],
l=config['kernel_l'],
phi=phi)
if config['grasp_symmetry']:
def swapped_phi(rv):
return rv.swapped_features
nn = kernels.SymmetricKDTree(phi=phi, alternate_phi=swapped_phi)
kernel = kernels.SymmetricSquaredExponentialKernel(
sigma=config['kernel_sigma'],
l=config['kernel_l'],
phi=phi,
alternate_phi=swapped_phi)
objective = objectives.RandomBinaryObjective()
# pre-computed pfc values
estimated_pfc = np.array([c.grasp.quality for c in candidates])
# uniform allocation baseline
ua = das.UniformAllocationMean(objective, candidates)
logging.info('Running uniform allocation.')
ua_result = ua.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# Thompson sampling for faster convergence
ts = das.ThompsonSampling(objective, candidates)
logging.info('Running Thompson sampling.')
ts_result = ts.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
# correlated Thompson sampling for even faster convergence
ts_corr = das.CorrelatedThompsonSampling(
objective,
candidates,
nn,
kernel,
tolerance=config['kernel_tolerance'])
logging.info('Running correlated Thompson sampling.')
ts_corr_result = ts_corr.solve(
termination_condition=tc.OrTerminationCondition(tc_list),
snapshot_rate=snapshot_rate)
object_grasps = [candidates[i].grasp for i in ts_result.best_candidates]
grasp_qualities = list(ts_result.best_pred_means)
bandit_stop = time.clock()
logging.info('Bandits took %f sec' % (bandit_stop - bandit_start))
# get rotated, translated versions of grasps
delay = 0
pr2_grasps = []
pr2_grasp_qualities = []
theta_res = config['grasp_theta_res'] * np.pi
# grasp_checker = pgc.OpenRaveGraspChecker(view=config['vis_grasps'])
if config['vis_grasps']:
delay = config['vis_delay']
for grasp, grasp_quality in zip(object_grasps, grasp_qualities):
rotated_grasps = grasp.transform(obj.tf, theta_res)
# rotated_grasps = grasp_checker.prune_grasps_in_collision(obj, rotated_grasps, auto_step=True, close_fingers=False, delay=delay)
pr2_grasps.extend(rotated_grasps)
pr2_grasp_qualities.extend([grasp_quality] * len(rotated_grasps))
logging.info('Num grasps: %d' % (len(pr2_grasps)))
grasp_filename = os.path.join(dest, obj.key + '.json')
with open(grasp_filename, 'w') as f:
jsons.dump([
g.to_json(quality=q)
for g, q in zip(pr2_grasps, pr2_grasp_qualities)
], f)
ua_normalized_reward = reward_vs_iters(ua_result, estimated_pfc)
ts_normalized_reward = reward_vs_iters(ts_result, estimated_pfc)
ts_corr_normalized_reward = reward_vs_iters(ts_corr_result, estimated_pfc)
return BanditCorrelatedExperimentResult(ua_normalized_reward,
ts_normalized_reward,
ts_corr_normalized_reward,
estimated_pfc,
ua_result.iters,
kernel.matrix(candidates),
obj_key=obj.key)
def extract_features(obj, dest, feature_dest, config):
# sample grasps
sample_start = time.clock()
if config['grasp_sampler'] == 'antipodal':
logging.info('Using antipodal grasp sampling')
sampler = ags.AntipodalGraspSampler(config)
grasps = sampler.generate_grasps(
obj, check_collisions=config['check_collisions'])
# pad with gaussian grasps
num_grasps = len(grasps)
min_num_grasps = config['min_num_grasps']
if num_grasps < min_num_grasps:
target_num_grasps = min_num_grasps - num_grasps
gaussian_sampler = gs.GaussianGraspSampler(config)
gaussian_grasps = gaussian_sampler.generate_grasps(
obj,
target_num_grasps=target_num_grasps,
check_collisions=config['check_collisions'])
grasps.extend(gaussian_grasps)
else:
logging.info('Using Gaussian grasp sampling')
sampler = gs.GaussianGraspSampler(config)
grasps = sampler.generate_grasps(
obj, check_collisions=config['check_collisions'])
sample_end = time.clock()
sample_duration = sample_end - sample_start
logging.info('Grasp candidate generation took %f sec' % (sample_duration))
if not grasps or len(grasps) == 0:
logging.info('Skipping %s' % (obj.key))
return
# compute all features
feature_start = time.clock()
feature_extractor = ff.GraspableFeatureExtractor(obj, config)
all_features = feature_extractor.compute_all_features(grasps)
feature_end = time.clock()
feature_duration = feature_end - feature_start
logging.info('Feature extraction took %f sec' % (feature_duration))
# generate pfc candidates
graspable_rv = pfc.GraspableObjectGaussianPose(obj, config)
f_rv = scipy.stats.norm(config['friction_coef'], config['sigma_mu'])
candidates = []
logging.info('%d grasps, %d valid features', len(grasps),
len(all_features) - all_features.count(None))
for grasp, features in zip(grasps, all_features):
logging.info('Adding grasp %d candidate' % (len(candidates)))
if features is None:
logging.info('No features computed.')
continue
grasp_rv = pfc.ParallelJawGraspGaussian(grasp, config)
pfc_rv = pfc.ForceClosureRV(grasp_rv, graspable_rv, f_rv, config)
pfc_rv.set_features(features)
candidates.append(pfc_rv)
logging.info('%d candidates', len(candidates))
# brute force with uniform allocation
brute_force_iter = config['bandit_brute_force_iter']
snapshot_rate = config['bandit_snapshot_rate']
def phi(rv):
return rv.features
objective = objectives.RandomBinaryObjective()
ua = das.UniformAllocationMean(objective, candidates)
logging.info('Running uniform allocation for true pfc.')
bandit_start = time.clock()
ua_result = ua.solve(
termination_condition=tc.MaxIterTerminationCondition(brute_force_iter),
snapshot_rate=snapshot_rate)
bandit_end = time.clock()
bandit_duration = bandit_end - bandit_start
logging.info('Uniform allocation (%d iters) took %f sec' %
(brute_force_iter, bandit_duration))
cand_grasps = [c.grasp for c in candidates]
cand_features = [c.features_ for c in candidates]
final_model = ua_result.models[-1]
estimated_pfc = models.BetaBernoulliModel.beta_mean(
final_model.alphas, final_model.betas)
if len(cand_grasps) != len(estimated_pfc):
logging.warning(
'Number of grasps does not match estimated pfc results.')
IPython.embed()
# write to file
grasp_filename = os.path.join(dest, obj.key + '.json')
with open(grasp_filename, 'w') as grasp_file:
jsons.dump([
g.to_json(quality=q, num_successes=a, num_failures=b)
for g, q, a, b in zip(cand_grasps, estimated_pfc,
final_model.alphas, final_model.betas)
], grasp_file)
# HACK to make paths relative
features_as_json = [f.to_json(feature_dest) for f in cand_features]
output_dest = os.path.split(dest)[0]
for feature_as_json in features_as_json:
feature_as_json = list(feature_as_json.values())[0]
for wname in ('w1', 'w2'):
wdata = feature_as_json[wname]
for k, v in wdata.items():
wdata[k] = os.path.relpath(
v, output_dest) # relative to output_dest
feature_filename = os.path.join(feature_dest, obj.key + '.json')
with open(feature_filename, 'w') as feature_file:
jsons.dump(features_as_json, feature_file)
