neuro-symbolic-visual-dialog/utils_preprocess.py

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2022-08-10 16:49:55 +02:00
import os
import json
import numpy as np
import torch
def mkdirs(paths):
if isinstance(paths, list):
for path in paths:
if not os.path.exists(path):
os.makedirs(path)
else:
if not os.path.exists(paths):
os.makedirs(paths)
def invert_dict(d):
return {v: k for k, v in d.items()}
def load_vocab(path):
with open(path, 'r') as f:
vocab = json.load(f)
vocab['question_idx_to_token'] = invert_dict(vocab['question_token_to_idx'])
vocab['program_idx_to_token'] = invert_dict(vocab['program_token_to_idx'])
vocab['answer_idx_to_token'] = invert_dict(vocab['answer_token_to_idx'])
# Sanity check: make sure <NULL>, <START>, and <END> are consistent
assert vocab['question_token_to_idx']['<NULL>'] == 0
assert vocab['question_token_to_idx']['<START>'] == 1
assert vocab['question_token_to_idx']['<END>'] == 2
assert vocab['program_token_to_idx']['<NULL>'] == 0
assert vocab['program_token_to_idx']['<START>'] == 1
assert vocab['program_token_to_idx']['<END>'] == 2
return vocab
def load_scenes(scenes_json):
with open(scenes_json) as f:
scenes_dict = json.load(f)['scenes']
scenes = []
for s in scenes_dict:
table = []
for i, o in enumerate(s['objects']):
item = {}
item['id'] = '%d-%d' % (s['image_index'], i)
if '3d_coords' in o:
item['position'] = [np.dot(o['3d_coords'], s['directions']['right']),
np.dot(o['3d_coords'], s['directions']['front']),
o['3d_coords'][2]]
else:
item['position'] = o['position']
item['color'] = o['color']
item['material'] = o['material']
item['shape'] = o['shape']
item['size'] = o['size']
table.append(item)
scenes.append(table)
return scenes
def load_embedding(path):
return torch.Tensor(np.load(path))