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 test_correlated_thompson_sampling(num_candidates=NUM_CANDIDATES,
sig=1.0,
eps=0.5):
# get candidates
actual_means = np.linspace(0.0, 1.0, num=num_candidates)
candidates = [BernoulliRV(m) for m in actual_means]
# get true maximum
true_max = np.max(actual_means)
true_max_indices = np.where(actual_means == true_max)
# constructing nearest neighbor and kernel
def phi(bern):
return np.array([round(bern.p(), 2)])
nn = kernels.KDTree(phi=phi)
kernel = kernels.SquaredExponentialKernel(sigma=sig, phi=phi)
# solve using Thompson sampling
obj = objectives.RandomBinaryObjective()
ts = CorrelatedThompsonSampling(obj, candidates, nn, kernel, tolerance=eps)
result = ts.solve(
termination_condition=tc.MaxIterTerminationCondition(MAX_ITERS),
snapshot_rate=SNAPSHOT_RATE)
# check result (not guaranteed to work in finite iterations but whatever)
assert len(result.best_candidates) == 1
assert np.abs(result.best_candidates[0].p() - true_max) < 1e-4
logging.info('Correlated Thompson sampling test passed!')
logging.info('Took %f sec' % (result.total_time))
logging.info('Best index %d' % (true_max_indices[0]))
info = u' (σ=%.1f, ɛ=%.3f)' % (sig, eps)
# visualize result
plot_num_pulls(result)
plt.title('Observations Per Variable for Correlated Thompson Sampling' +
info)
plot_value_vs_time(result, candidates, true_max)
plt.title('P(Success) versus Iterations for Correlated Thompson Sampling' +
info)
return result
def __init__(self, feature_db, pca_components=10, feature_vectors=None, svd=None, neighbor_tree=None, neighbor_data=None): if feature_vectors == None: feature_vectors = feature_db.feature_vectors() #feature_db.train_feature_vectors() self.pca_components = pca_components if svd == None: self.svd = self._create_train_svd(feature_vectors, pca_components) data_size = len(feature_vectors.keys()) if self.pca_components > data_size: self.pca_components = data_size else: self.svd = svd if neighbor_tree == None: start = time.time() data = self._project_feature_vectors(feature_vectors) self.neighbors = self._create_neighbors(data) end = time.time() print 'TIME: %0.4f' % (end - start) else: self.neighbors = kernels.KDTree(phi=lambda x: x.feature_vector) self.neighbors.tree_ = neighbor_tree self.neighbors.data_ = neighbor_data
def _compute_priors_with_neighbor_vectors(self,
obj,
feature_vector,
candidates,
neighbor_vector_dict,
grasp_transfer_method=0,
alpha_prior=1.0,
beta_prior=1.0):
# load in all grasps and features
logging.info('Loading features...')
start_time = time.time()
# transfer features
logging.info('Using grasp transfer method %d' %
(grasp_transfer_method))
if grasp_transfer_method == self.GRASP_TRANSFER_METHOD_SHOT:
reg_solver_dict = {}
for neighbor_key in neighbor_vector_dict:
reg_solver_dict[neighbor_key] = self._registration_solver(
obj, neighbor_obj)
self.reg_solver_dict = reg_solver_dict
elif grasp_transfer_method == self.GRASP_TRANSFER_METHOD_SCALE_XYZ:
self._create_feature_scales_xyz(obj, neighbor_vector_dict.keys())
elif grasp_transfer_method == self.GRASP_TRANSFER_METHOD_SCALE_SINGLE:
self._create_feature_scales_single(obj,
neighbor_vector_dict.keys())
transfer_time = time.time()
logging.info('Grasp transfer precomp took %f sec' %
(transfer_time - start_time))
# extract all neighbor properties
neighbor_key_list = neighbor_vector_dict.keys()
neighbor_index_list = []
neighbor_feature_list = []
neighbor_grasp_list = []
neighbor_kernel_list = []
neighbor_distance_list = []
for k, neighbor_key in enumerate(neighbor_vector_dict):
if neighbor_key == obj.key:
continue
logging.info('Loading features for %s' % (neighbor_key))
neighbor_obj = self.db[neighbor_key]
grasps, neighbor_features = self._load_grasps_and_features(
neighbor_obj)
# put stuff in a list
neighbor_index_list.extend([k] * len(neighbor_features))
neighbor_kernel = self.neighbor_kernel.evaluate(
feature_vector, neighbor_vector_dict[neighbor_key])
neighbor_distance = np.linalg.norm(
feature_vector - neighbor_vector_dict[neighbor_key])
logging.info('Distance %f' % (neighbor_distance))
if neighbor_kernel < self.prior_kernel_tolerance:
neighbor_kernel = 0
neighbor_kernel_list.extend([neighbor_kernel] *
len(neighbor_features))
neighbor_distance_list.append(neighbor_distance)
neighbor_grasp_list.extend(grasps)
# transfer all features
f_list = []
for features, grasp in zip(neighbor_features, grasps):
f_list.append(
self._transfer_features(features, grasp, neighbor_key,
grasp_transfer_method))
neighbor_feature_list.extend([f.phi for f in f_list])
lf_time = time.time()
logging.info('Loading features took %f sec' %
(lf_time - transfer_time))
# create nn struct with neighbor features
def phi(x):
return x
error_radius = self.grasp_kernel.error_radius(
self.grasp_kernel_tolerance)
nn = kernels.KDTree(phi=phi)
nn.train(neighbor_feature_list)
logging.info('Num total features %d' % (len(neighbor_feature_list)))
# create priors using the nn struct
logging.info('Creating priors')
prior_compute_start = time.clock()
all_neighbor_kernels = [[]] * len(neighbor_key_list)
all_neighbor_pfc_diffs = [[]] * len(neighbor_key_list)
alpha_priors = []
beta_priors = []
num_neighbors = []
out_rate = 50
for k, candidate in enumerate(candidates):
alpha = alpha_prior
beta = beta_prior
if k % out_rate == 0:
logging.info('Creating priors for candidate %d' % (k))
# get neighbors within distance and compute kernels
neighbor_indices, _ = nn.within_distance(candidate.features,
error_radius,
return_indices=True)
num_neighbors.append(len(neighbor_indices))
for index in neighbor_indices:
successes = neighbor_grasp_list[index].successes
failures = neighbor_grasp_list[index].failures
object_kernel = neighbor_kernel_list[index]
grasp_kernel = self.grasp_kernel(candidate.features,
neighbor_feature_list[index])
kernel_val = object_kernel * grasp_kernel
all_neighbor_kernels[neighbor_index_list[index]].append(
kernel_val)
all_neighbor_pfc_diffs[neighbor_index_list[index]].append(
abs(candidate.grasp.quality -
neighbor_grasp_list[index].quality))
alpha += kernel_val * successes
beta += kernel_val * failures
alpha_priors.append(alpha)
beta_priors.append(beta)
prior_compute_end = time.clock()
logging.info('Created priors in %f sec' %
(prior_compute_end - prior_compute_start))
return alpha_priors, beta_priors, neighbor_key_list, neighbor_distance_list, all_neighbor_kernels, all_neighbor_pfc_diffs, num_neighbors
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 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 eval_hyperparams(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)
# create hyperparam dict
num_grasps = len(candidates)
hyperparams = {}
hyperparams['weight_grad'] = config['weight_grad_x']
hyperparams['weight_moment'] = config['weight_gravity']
hyperparams['weight_shape'] = config['prior_neighbor_weight']
hyperparams['num_neighbors'] = config['prior_num_neighbors']
return HyperparamEvalResult(ce_vals,
se_vals,
we_vals,
ccbp_vals,
num_grasps,
total_weights,
hyperparams,
prior_comp_times,
obj_key=obj.key,
neighbor_keys=neighbor_keys)
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 __init__(self, config):
self.feature_extractor_ = CNNBatchFeatureExtractor(config)
matcher = kernels.KDTree(phi=lambda x: x.descriptor)
self._parse_config(config)
di.Hdf5DatabaseIndexer.__init__(self, matcher)
def _create_neighbors(self, feature_vectors): feature_objects = map(FeatureObject, feature_vectors.keys(), feature_vectors.values()) neighbors = kernels.KDTree(phi=lambda x: x.feature_vector) neighbors.train(feature_objects) return neighbors
