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