def get_mean_std(
scenes,
data_name = 'images.hdf5',
datadir = '../mixed_offline_data/',
):
scene_names = get_scene_names(scenes)
trans = T.Compose([
T.ToTensor(),
#T.Normalize(mean=[0,0,0],std=[1,1,1],inplace=True)
])
means = []
stds = []
pbar = tqdm(total = states_num(scenes, preload=data_name))
for s in scene_names:
data = {}
loader = h5py.File(os.path.join(datadir,s,data_name),"r",)
num = len(list(loader.keys()))
for k in loader.keys():
vobs = trans(loader[k][:])
#vobs = torch.tensor(loader[k][:])
means.append(torch.mean(vobs, dim=[1,2]))
stds.append(torch.std(vobs, dim=[1,2]))
pbar.update(1)
loader.close()
mean = torch.mean(torch.stack(means), dim=0)
std = torch.std(torch.stack(stds), dim=0)
return dict(mean=mean,std=std)
json.dump(ips, f)
test_scenes = {
'kitchen': range(1, 21),
'living_room': range(1, 21),
'bedroom': range(1, 21),
'bathroom': range(1, 21),
}
out_name = 'min_len.json'
graph_name = 'nop_graph.json'
visible_file_name = 'visible_object_map.json'
datadir = '../mixed_offline_data/'
scene_names = get_scene_names(test_scenes)
print(f'making for {len(scene_names)} scenes')
p = tqdm(total=len(scene_names))
for scene_name in scene_names:
p.update(1)
with open(
os.path.join(datadir, scene_name, graph_name),
"r",
) as f:
graph_json = json.load(f)
with open(
os.path.join(datadir, scene_name, visible_file_name),
"r",
'living_room': range(1, 21),
'bedroom': range(1, 21),
'bathroom': range(1, 21),
}
image_name = 'images.hdf5'
datadir = '../mixed_offline_data/'
fc_name = 'resnet50fc.hdf5'
score_name = 'resnet50score'
trans = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.5265, 0.4560, 0.3682],
std=[0.0540, 0.0554, 0.0567],
inplace=True)
])
scene_names = get_scene_names(scenes)
resmodel = my_resnet50().cuda()
print(f'making for {len(scene_names)} scenes')
pbar = tqdm(total=states_num(scenes, preload=image_name))
for scene_name in scene_names:
fc_writer = h5py.File(os.path.join(datadir, scene_name, fc_name), 'w')
score_writer = h5py.File(os.path.join(datadir, scene_name, score_name),
'w')
RGBloader = h5py.File(
os.path.join(datadir, scene_name, image_name),
"r",
)
for k in RGBloader.keys():