def __getitem__(self, idx):
lbl = self.lbl_list[idx]
pc = np.load(self.pc_list[idx])[:self.pc_input_num].astype(np.float32)
pc = normal_pc(pc)
if self.status == STATUS_TRAIN:
pc = pc_aug_funs(pc)
pc = np.expand_dims(pc.transpose(), axis=2)
return torch.from_numpy(pc).float(), lbl
def __getitem__(self, idx):
views = [self.transform(Image.open(v)) for v in self.view_list[idx]]
lbl = self.lbl_list[idx]
pc = np.load(self.pc_list[idx])[:self.pc_input_num].astype(np.float32)
pc = normal_pc(pc)
if self.status == STATUS_TRAIN:
pc = pc_aug_funs(pc)
pc = np.expand_dims(pc.transpose(), axis=2)
return torch.stack(views).float(), torch.from_numpy(pc).float(), lbl
def __getitem__(self, idx):
names = osp.split(self.pc_list[idx])[1].split('.')[0]
views = [self.transform(Image.open(v)) for v in self.view_list[idx]]
depth = [
self.transform(Image.open(v).convert('RGB'))
for v in self.dp_list[idx]
]
lbl = self.lbl_list[idx]
pc = np.load(self.pc_list[idx])[:self.pc_input_num].astype(np.float32)
pc = normal_pc(pc)
# if self.status == STATUS_TRAIN:
pc = pc_aug_funs(pc)
pc = np.expand_dims(pc.transpose(), axis=2)
# return torch.stack(views).float(), torch.from_numpy(pc).float(), lbl, names
return torch.stack(views).float(), torch.stack(
depth).float(), torch.from_numpy(pc).float(), lbl