62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import os
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import json
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import numpy as np
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import torch
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def mkdirs(paths):
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if isinstance(paths, list):
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for path in paths:
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if not os.path.exists(path):
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os.makedirs(path)
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else:
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if not os.path.exists(paths):
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os.makedirs(paths)
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def invert_dict(d):
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return {v: k for k, v in d.items()}
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def load_vocab(path):
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with open(path, 'r') as f:
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vocab = json.load(f)
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vocab['question_idx_to_token'] = invert_dict(vocab['question_token_to_idx'])
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vocab['program_idx_to_token'] = invert_dict(vocab['program_token_to_idx'])
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vocab['answer_idx_to_token'] = invert_dict(vocab['answer_token_to_idx'])
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# Sanity check: make sure <NULL>, <START>, and <END> are consistent
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assert vocab['question_token_to_idx']['<NULL>'] == 0
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assert vocab['question_token_to_idx']['<START>'] == 1
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assert vocab['question_token_to_idx']['<END>'] == 2
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assert vocab['program_token_to_idx']['<NULL>'] == 0
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assert vocab['program_token_to_idx']['<START>'] == 1
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assert vocab['program_token_to_idx']['<END>'] == 2
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return vocab
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def load_scenes(scenes_json):
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with open(scenes_json) as f:
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scenes_dict = json.load(f)['scenes']
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scenes = []
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for s in scenes_dict:
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table = []
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for i, o in enumerate(s['objects']):
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item = {}
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item['id'] = '%d-%d' % (s['image_index'], i)
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if '3d_coords' in o:
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item['position'] = [np.dot(o['3d_coords'], s['directions']['right']),
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np.dot(o['3d_coords'], s['directions']['front']),
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o['3d_coords'][2]]
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else:
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item['position'] = o['position']
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item['color'] = o['color']
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item['material'] = o['material']
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item['shape'] = o['shape']
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item['size'] = o['size']
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table.append(item)
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scenes.append(table)
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return scenes
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def load_embedding(path):
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return torch.Tensor(np.load(path)) |