def _buildOb(self):
state, adj_loc_ls = env._get_panorama_states(self.sim)
assert self.scanId== state.scanId
#filePath = 'img_features_36*2048/'+ self.scanId + '/' + state.location.viewpointId + '.pt'
#feature = torch.load(filePath)
feature = [f.get_features(state) for f in image_features_list]
#print(feature,_static_loc_embeddings[state.viewIndex])
#print(feature.size(),_static_loc_embeddings[state.viewIndex].size())
feature_with_loc = np.concatenate((feature[0], _static_loc_embeddings[state.viewIndex]), axis=-1)
action_embedding = env._build_action_embedding(adj_loc_ls, feature[0])
ob = {
'scan' : state.scanId,
'viewpoint' : state.location.viewpointId,
'viewIndex' : state.viewIndex,
'heading' : state.heading,
'elevation' : state.elevation,
'feature' : [feature_with_loc],
'step' : state.step,
'adj_loc_list' : adj_loc_ls,
'action_embedding': action_embedding,
'navigableLocations' : state.navigableLocations,
}
return ob
def roll_out(scan, viewpoint, sim=sim, heading = 0, elevation = 0):
sim.newEpisode(scan, viewpoint, heading, elevation)
ended = False
traj = []
traj.append(viewpoint)
for i in range(step):
_, adj_loc_ls = env._get_panorama_states(sim)
if len(adj_loc_ls) == 1:
ended == True
elif i == 0:
next_loc = random.choice(adj_loc_ls[1:])
nextViewpointId = next_loc['nextViewpointId']
nextViewIndex = next_loc['absViewIndex']
env._navigate_to_location(sim, nextViewpointId,nextViewIndex)
traj.append(nextViewpointId)
else:
next_loc = adj_loc_ls[1]
nextViewpointId = next_loc['nextViewpointId']
nextViewIndex = next_loc['absViewIndex']
env._navigate_to_location(sim, nextViewpointId,nextViewIndex)
traj.append(nextViewpointId)
if ended :
print(adj_loc_ls)
break
return traj
def buildOb(self):
state, adj_loc_ls = env._get_panorama_states(self.sim)
assert self.scanId == state.scanId
filePath = 'img_features_36*2048/' + self.scanId + '/' + state.location.viewpointId + '.pt'
feature_pano = torch.load(filePath)
feature = feature_pano[state.viewIndex]
assert len(feature) == 1, 'for now, only work with MeanPooled feature'
feature_with_loc = np.concatenate(
(feature, _static_loc_embeddings[state.viewIndex]), axis=-1)
action_embedding = env._build_action_embedding(adj_loc_list,
feature_pano)
ob = {
'instr_id': item['instr_id'],
'scan': state.scanId,
'viewpoint': state.location.viewpointId,
'viewIndex': state.viewIndex,
'heading': state.heading,
'elevation': state.elevation,
'feature': [feature_with_loc],
'step': state.step,
'adj_loc_list': adj_loc_list,
'action_embedding': action_embedding,
'navigableLocations': state.navigableLocations,
}
return ob
def _get_adj_loc_ls(self):
_, adj_loc_ls = env._get_panorama_states(self.sim)
return adj_loc_ls