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merge_tokens.py
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merge_tokens.py
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import json
import torch
from collections import Counter, defaultdict
def load_ent_map(dataset):
ent_map = {}
if dataset == "DB15K":
f = open("data/{}/entities.txt".format(dataset), "r")
for line in f.readlines():
ent = line.replace('\n', '')
ent_map[ent] = ent
else:
f = open("data/{}/entity2id.txt".format(dataset), "r")
for line in f.readlines():
ent, id = line[:-1].split(' ')
ent_map[ent] = int(id)
return ent_map
def get_entity_visual_tokens(dataset, max_num):
tokenized_result = json.load(open("tokens/{}-visual.json".format(dataset), "r"))
token_dict = defaultdict(list)
entity_dict = load_ent_map(dataset)
for i in range(8192 + 1):
token_dict[i] = []
for entity in tokenized_result:
token_count = Counter(tokenized_result[entity])
selected_tokens = token_count.most_common(max_num)
for (token, num) in selected_tokens:
token_dict[token].append(entity)
num_count = [(k, len(token_dict[k])) for k in token_dict]
num_count = sorted(num_count, key=lambda x: -x[1])
token_ids = list(token_dict.keys())
# print(token_ids)
entity_to_token = defaultdict(list)
for i in range(len(token_ids)):
for ent in token_dict[token_ids[i]]:
entity_to_token[entity_dict[ent]].append(i)
# json.dump(entity_to_token, open("{}-tokens-{}.json".format(dataset, max_num), "w"))
entid_tokens = []
ent_key_mask = []
for i in range(len(entity_dict)):
key = str(i) if dataset == "DB15K" else i
if entity_to_token[key] != []:
entid_tokens.append(entity_to_token[key])
ent_key_mask.append(([False] * max_num))
else:
entid_tokens.append([8192] * max_num)
ent_key_mask.append(([True] * max_num))
return torch.LongTensor(entid_tokens), torch.BoolTensor(ent_key_mask).cuda()
def get_entity_visual_tokens_with_limit_db15k(dataset, max_num, max_img=None):
assert max_img is not None
tokenized_result = json.load(open("tokens/{}-visual.json".format(dataset), "r"))
token_dict = defaultdict(list)
entity_dict = load_ent_map(dataset)
for id in tokenized_result.keys():
tokenized_result[id] = tokenized_result[id][0: min(len(tokenized_result[id]), 196 * max_img)]
for i in range(8192 + 1):
token_dict[i] = []
for entity in tokenized_result:
token_count = Counter(tokenized_result[entity])
selected_tokens = token_count.most_common(max_num)
for (token, num) in selected_tokens:
token_dict[token].append(entity)
num_count = [(k, len(token_dict[k])) for k in token_dict]
num_count = sorted(num_count, key=lambda x: -x[1])
token_ids = list(token_dict.keys())
# print(token_ids)
entity_to_token = defaultdict(list)
for i in range(len(token_ids)):
for ent in token_dict[token_ids[i]]:
entity_to_token[entity_dict[ent]].append(i)
# json.dump(entity_to_token, open("{}-tokens-{}.json".format(dataset, max_num), "w"))
entid_tokens = []
ent_key_mask = []
for i in range(len(entity_dict)):
if entity_to_token[str(i)] != []:
entid_tokens.append(entity_to_token[str(i)])
ent_key_mask.append(([False] * max_num))
else:
entid_tokens.append([8192] * max_num)
ent_key_mask.append(([True] * max_num))
return torch.LongTensor(entid_tokens), torch.BoolTensor(ent_key_mask).cuda()
def get_entity_visual_tokens_with_limit(dataset, max_num, max_img=None):
if dataset == "DB15K":
return get_entity_visual_tokens_with_limit_db15k(dataset, max_num, max_img)
assert max_img is not None
tokenized_result = json.load(open("tokens/{}-visual-v2.json".format(dataset), "r"))
token_dict = defaultdict(list)
entity_dict = load_ent_map(dataset)
for id in tokenized_result.keys():
tokenized_result[id] = tokenized_result[id][0: min(len(tokenized_result[id]), 196 * max_img)]
for i in range(8192 + 1):
token_dict[i] = []
for entity in tokenized_result:
token_count = Counter(tokenized_result[entity])
selected_tokens = token_count.most_common(max_num)
for (token, num) in selected_tokens:
token_dict[token].append(entity)
num_count = [(k, len(token_dict[k])) for k in token_dict]
num_count = sorted(num_count, key=lambda x: -x[1])
token_ids = list(token_dict.keys())
entity_to_token = defaultdict(list)
for i in range(len(token_ids)):
for ent in token_dict[token_ids[i]]:
entity_to_token[entity_dict[ent]].append(i)
entid_tokens = []
ent_key_mask = []
for i in range(len(entity_dict)):
if entity_to_token[i] != []:
entid_tokens.append(entity_to_token[i])
ent_key_mask.append(([False] * max_num))
else:
entid_tokens.append([8192] * max_num)
ent_key_mask.append(([True] * max_num))
return torch.LongTensor(entid_tokens), torch.BoolTensor(ent_key_mask).cuda()
def get_entity_textual_tokens_db15K(dataset, max_num):
tokenized_result = json.load(open("tokens/{}-textual-v2.json".format(dataset), "r"))
token_dict = defaultdict(list)
entity_dict = load_ent_map(dataset)
for i in range(30522 + 1):
token_dict[i] = []
for entity in tokenized_result:
token_count = Counter(tokenized_result[entity])
selected_tokens = token_count.most_common(max_num)
for (token, num) in selected_tokens:
token_dict[token].append(entity)
num_count = [(k, len(token_dict[k])) for k in token_dict]
num_count = sorted(num_count, key=lambda x: -x[1])
token_ids = list(token_dict.keys())
# print(token_ids)
entity_to_token = defaultdict(list)
for i in range(len(token_ids)):
for ent in token_dict[token_ids[i]]:
entity_to_token[entity_dict[ent]].append(i)
entid_tokens = []
ent_key_mask = []
for i in range(len(entity_dict)):
if entity_to_token[str(i)] == max_num:
entid_tokens.append(entity_to_token[str(i)])
ent_key_mask.append(([False] * max_num))
else:
s = entity_to_token[str(i)]
entid_tokens.append(s + [14999] * (max_num - len(s)))
ent_key_mask.append(([False] * len(s) + [True] * (max_num - len(s))))
return torch.LongTensor(entid_tokens), torch.BoolTensor(ent_key_mask).cuda()
def get_entity_textual_tokens(dataset, max_num):
if dataset == "DB15K":
return get_entity_textual_tokens_db15K(dataset, max_num)
tokenized_result = json.load(open("tokens/{}-textual.json".format(dataset), "r"))
token_dict = defaultdict(list)
entity_dict = load_ent_map(dataset)
for i in range(30522 + 1):
token_dict[i] = []
for entity in tokenized_result:
token_count = Counter(tokenized_result[entity])
selected_tokens = token_count.most_common(max_num)
for (token, num) in selected_tokens:
token_dict[token].append(entity)
num_count = [(k, len(token_dict[k])) for k in token_dict]
num_count = sorted(num_count, key=lambda x: -x[1])
token_ids = list(token_dict.keys())
entity_to_token = defaultdict(list)
for i in range(len(token_ids)):
for ent in token_dict[token_ids[i]]:
entity_to_token[entity_dict[ent]].append(i)
entid_tokens = []
ent_key_mask = []
for i in range(len(entity_dict)):
if entity_to_token[i] == max_num:
entid_tokens.append(entity_to_token[i])
ent_key_mask.append(([False] * max_num))
else:
s = entity_to_token[i]
entid_tokens.append(s + [14999] * (max_num - len(s)))
ent_key_mask.append(([False] * len(s) + [True] * (max_num - len(s))))
return torch.LongTensor(entid_tokens), torch.BoolTensor(ent_key_mask).cuda()
def get_entity_textual_tokens_FB15K237(dataset, max_num):
assert dataset == "FB15K-237"
tokenized_result = json.load(open("tokens/{}-textual.json".format(dataset), "r"))
token_dict = defaultdict(list)
entity_dict = load_ent_map(dataset)
for i in range(30522 + 1):
token_dict[i] = []
for entity in tokenized_result:
token_count = Counter(tokenized_result[entity])
selected_tokens = token_count.most_common(max_num)
for (token, num) in selected_tokens:
token_dict[token].append(entity)
num_count = [(k, len(token_dict[k])) for k in token_dict]
num_count = sorted(num_count, key=lambda x: -x[1])
token_ids = list(token_dict.keys())
entity_to_token = defaultdict(list)
for i in range(len(token_ids)):
for ent in token_dict[token_ids[i]]:
entity_to_token[entity_dict[ent]].append(i)
entid_tokens = []
ent_key_mask = []
for i in entity_dict:
if entity_to_token[i] == max_num:
entid_tokens.append(entity_to_token[ent])
ent_key_mask.append(([False] * max_num))
else:
s = entity_to_token[i]
entid_tokens.append(s + [14999] * (max_num - len(s)))
ent_key_mask.append(([False] * len(s) + [True] * (max_num - len(s))))
return torch.LongTensor(entid_tokens), torch.BoolTensor(ent_key_mask).cuda()
def get_entity_visual_tokens_FB15K237(dataset, max_num):
assert dataset == "FB15K-237"
tokenized_result = json.load(open("tokens/{}-visual.json".format(dataset), "r"))
token_dict = defaultdict(list)
entity_dict = load_ent_map(dataset)
for i in range(8192 + 1):
token_dict[i] = []
for entity in tokenized_result:
token_count = Counter(tokenized_result[entity])
selected_tokens = token_count.most_common(max_num)
for (token, num) in selected_tokens:
token_dict[token].append(entity)
num_count = [(k, len(token_dict[k])) for k in token_dict]
num_count = sorted(num_count, key=lambda x: -x[1])
token_ids = list(token_dict.keys())
# print(token_ids)
entity_to_token = defaultdict(list)
for i in range(len(token_ids)):
for ent in token_dict[token_ids[i]]:
entity_to_token[entity_dict[ent]].append(i)
# json.dump(entity_to_token, open("{}-tokens-{}.json".format(dataset, max_num), "w"))
entid_tokens = []
ent_key_mask = []
for i in entity_dict:
key = i
if entity_to_token[key] != []:
entid_tokens.append(entity_to_token[key])
ent_key_mask.append(([False] * max_num))
else:
entid_tokens.append([8192] * max_num)
ent_key_mask.append(([True] * max_num))
return torch.LongTensor(entid_tokens), torch.BoolTensor(ent_key_mask).cuda()
if __name__ == "__main__":
dataset = "DB15K"
max_token_num = 8
a, b = get_entity_visual_tokens_FB15K237(dataset, max_token_num)
print(a)