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import os
import pandas as pd
import pickle
import numpy as np
from torch.utils.data import Dataset
CLASSES = ["Bag", "Bed", "Bowl","Clock", "Dishwasher", "Display", "Door", "Earphone", "Faucet",
"Hat", "StorageFurniture", "Keyboard", "Knife", "Laptop", "Microwave", "Mug",
"Refrigerator", "Chair", "Scissors", "Table", "TrashCan", "Vase", "Bottle"]
AFFORD_CL = ['lay','sit','support','grasp','lift','contain','open','wrap_grasp','pour',
'move','display','push','pull','listen','wear','press','cut','stab']
def pc_normalize(pc):
centroid = np.mean(pc, axis=0)
pc = pc - centroid
m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
pc = pc / m
return pc, centroid, m
class Corrupt(Dataset):
def __init__(self,
corrupt_type='scale',
level=0
):
#replace with the path to the LASO-C/PIAD-C dataset
data_root='LASO-C'
file_name = f'{corrupt_type}_{level}.pkl'
self.corrupt_type = corrupt_type
self.level = level
self.cls2idx = {cls.lower():np.array(i).astype(np.int64) for i, cls in enumerate(CLASSES)}
self.aff2idx = {cls:np.array(i).astype(np.int64) for i, cls in enumerate(AFFORD_CL)}
with open(os.path.join(data_root, 'point', file_name), 'rb') as f:
self.anno = pickle.load(f)
self.question_df = pd.read_csv(os.path.join(data_root, 'text', 'Affordance-Question.csv'))
def find_rephrase(self, df, object_name, affordance):
qid = 'Question0'
result = df.loc[(df['Object'] == object_name) & (df['Affordance'] == affordance), [qid]]
if not result.empty:
return result.iloc[0][qid]
else:
raise NotImplementedError
def __getitem__(self, index):
data = self.anno[index]
cls = data['class']
affordance = data['affordance']
gt_mask = data['mask']
point_set = data['point']
point_set,_,_ = pc_normalize(point_set)
question = self.find_rephrase(self.question_df, cls, affordance)
affordance = self.aff2idx[affordance]
point_input = point_set.transpose()
return point_input, self.cls2idx[cls], gt_mask, question, affordance
def __len__(self):
return len(self.anno)
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