class LandmarkFeats(MulFeats):
def __init__(self, actionfile):
MulFeats.__init__(self, actionfile)
self.landmark_ctx = None
def set_landmark_file(self, landmarkf):
self.landmark_ctx = GPUContext()
self.landmark_ctx.read_h5(landmarkf)
self.landmark_targ_ctx = TgtContext(self.landmark_ctx)
self.weights = np.zeros(self.src_ctx.N + self.landmark_ctx.N +
MulFeats.N_costs)
def features(self, state, **kwargs):
mul_feats = MulFeats.features(self, state)
self.landmark_targ_ctx.set_cld(state.cloud)
landmark_feats = batch_tps_rpm_bij(self.landmark_ctx,
self.landmark_targ_ctx)
landmark_feats = np.exp(-landmark_feats)
landmark_feats /= np.sum(landmark_feats)
self.costs = np.c_[mul_feats,
np.tile(landmark_feats, (self.src_ctx.N, 1))]
return self.costs
@staticmethod
def get_size(num_actions, num_landmarks=70):
return num_actions + num_landmarks + MulFeats.N_costs
class BatchRCFeats(Feature):
def __init__(self, actionfile, action_list=[]):
self.src_ctx = SrcContext()
self.src_ctx.read_h5(actionfile)
self.tgt_cld = None
self.tgt_ctx = TgtContext(self.src_ctx)
self.name2ind = dict([(s, i)
for i, s in enumerate(self.src_ctx.seg_names)])
self.costs = np.zeros(self.src_ctx.N)
self.N = len(self.src_ctx.seg_names)
self.indicators = np.eye(self.N)
self.weights = np.r_[-1, np.zeros(self.N)]
self.w0 = 0
def features(self, state, **kwargs):
self.tgt_cld = state.cloud
self.tgt_ctx.set_cld(self.tgt_cld)
self.costs = batch_tps_rpm_bij(self.src_ctx, self.tgt_ctx)
return np.c_[self.costs, self.indicators]
def set_name2ind(self, action_list):
self.name2ind = dict([(s, i) for i, s in enumerate(action_list)])
def get_ind(self, a):
return self.name2ind[a]
@staticmethod
def get_size(num_actions):
return num_actions + 1
class BatchRCFeats(Feature):
def __init__(self, actionfile, action_list=[]):
self.src_ctx = SrcContext()
self.src_ctx.read_h5(actionfile)
self.tgt_cld = None
self.tgt_ctx = TgtContext(self.src_ctx)
self.name2ind = dict([(s, i) for i, s in enumerate(self.src_ctx.seg_names)])
self.costs = np.zeros(self.src_ctx.N)
self.N = len(self.src_ctx.seg_names)
self.indicators = np.eye(self.N)
self.weights = np.r_[-1, np.zeros(self.N)]
self.w0 = 0
def features(self, state, **kwargs):
self.tgt_cld = state.cloud
self.tgt_ctx.set_cld(self.tgt_cld)
self.costs = batch_tps_rpm_bij(self.src_ctx, self.tgt_ctx)
return np.c_[self.costs, self.indicators]
def set_name2ind(self, action_list):
self.name2ind = dict([(s, i) for i, s in enumerate(action_list)])
def get_ind(self, a):
return self.name2ind[a]
@staticmethod
def get_size(num_actions):
return num_actions + 1
def __init__(self, actionfile, action_list=[]):
self.src_ctx = SrcContext()
self.src_ctx.read_h5(actionfile)
self.tgt_cld = None
self.tgt_ctx = TgtContext(self.src_ctx)
self.name2ind = dict([(s, i)
for i, s in enumerate(self.src_ctx.seg_names)])
self.costs = np.zeros(self.src_ctx.N)
self.N = len(self.src_ctx.seg_names)
self.indicators = np.eye(self.N)
self.weights = np.r_[-1, np.zeros(self.N)]
self.w0 = 0
def batch_cost(self, test_scene_state):
if not(self.actionfile):
raise ValueError('No actionfile provided for gpu context')
tgt_ctx = TgtContext(self.src_ctx)
cloud = test_scene_state.cloud
cloud = self._clip_cloud(cloud)
tgt_ctx.set_cld(cloud)
cost_array = batch_tps_rpm_bij(self.src_ctx, tgt_ctx,
T_init=self.rad_init, T_final=self.rad_final,
outlierfrac=self.outlierfrac, outlierprior=self.outlierprior,
outliercutoff=settings.OUTLIER_CUTOFF,
em_iter=self.em_iter,
component_cost=True)
costs = dict(zip(self.src_ctx.seg_names, cost_array))
return costs
def __init__(self, actionfile, action_list=[]):
self.src_ctx = SrcContext()
self.src_ctx.read_h5(actionfile)
self.tgt_cld = None
self.tgt_ctx = TgtContext(self.src_ctx)
self.name2ind = dict([(s, i) for i, s in enumerate(self.src_ctx.seg_names)])
self.costs = np.zeros(self.src_ctx.N)
self.N = len(self.src_ctx.seg_names)
self.indicators = np.eye(self.N)
self.weights = np.r_[-1, np.zeros(self.N)]
self.w0 = 0
class LandmarkFeats(MulFeats):
def __init__(self, actionfile):
MulFeats.__init__(self, actionfile)
self.landmark_ctx = None
def set_landmark_file(self, landmarkf):
self.landmark_ctx = GPUContext()
self.landmark_ctx.read_h5(landmarkf)
self.landmark_targ_ctx = TgtContext(self.landmark_ctx)
self.weights = np.zeros(self.src_ctx.N + self.landmark_ctx.N + MulFeats.N_costs)
def features(self, state, **kwargs):
mul_feats = MulFeats.features(self, state)
self.landmark_targ_ctx.set_cld(state.cloud)
landmark_feats = batch_tps_rpm_bij(self.landmark_ctx, self.landmark_targ_ctx)
landmark_feats = np.exp(-landmark_feats)
landmark_feats /= np.sum(landmark_feats)
self.costs = np.c_[mul_feats, np.tile(landmark_feats, (self.src_ctx.N, 1))]
return self.costs
@staticmethod
def get_size(num_actions, num_landmarks=70):
return num_actions + num_landmarks + MulFeats.N_costs
def set_landmark_file(self, landmarkf):
self.landmark_ctx = GPUContext()
self.landmark_ctx.read_h5(landmarkf)
self.landmark_targ_ctx = TgtContext(self.landmark_ctx)
self.weights = np.zeros(self.src_ctx.N + self.landmark_ctx.N +
MulFeats.N_costs)
def set_landmark_file(self, landmarkf):
self.landmark_ctx = GPUContext()
self.landmark_ctx.read_h5(landmarkf)
self.landmark_targ_ctx = TgtContext(self.landmark_ctx)
self.weights = np.zeros(self.src_ctx.N + self.landmark_ctx.N + MulFeats.N_costs)