| 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 |
| ): |
| |
| |
| 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) |
|
|
