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init_task.py
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174 lines (166 loc) · 10.4 KB
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from generate import sequence_generator
from metrics.ciderD import CiderD
from ofa.tokenization_ofa import OFATokenizer
import torch
from data_utils.snli_ve_dataset import Trie
from data_utils.input_dataset import FileDataset as inputDataset
import pickle
import json, os
TOKENIZER_PATH = "./tokenizer"
def build_task(args):
tokenizer = OFATokenizer.from_pretrained(TOKENIZER_PATH)
args.tokenizer = tokenizer
args.tgt_dict = args.src_dict = {value: key for key, value in tokenizer.get_vocab().items()}
paths = args.tables.split(",")
args.test_dataset = inputDataset(paths[-1], args.selected_cols, data_slice=False)
if args.metric in ["cider", "ap", "acc"]:
args.best_score = 0
if args.task == 'pretrain':
args.pure_text_dataset = inputDataset(paths[-4], args.text_selected_cols)
args.pure_image_dataset = inputDataset(paths[-3], args.image_selected_cols, capacity=512)
args.detection_dataset = inputDataset(paths[-2], args.detection_selected_cols)
args.train_dataset = [inputDataset(paths[i], args.selected_cols) for i in range(len(paths) - 4)]
args.all_object_list = [
row.strip() for row in open(os.path.join(args.neg_sample_dir, 'object.txt')) if row.strip() != ''
]
args.all_relation_list = [
row.strip() for row in open(os.path.join(args.neg_sample_dir, 'relation.txt')) if row.strip() != ''
]
args.all_caption_list = [
row.strip() for row in open(os.path.join(args.neg_sample_dir, 'all_captions.txt')) if row.strip() != ''
]
args.type2attr_dict = {
'material': ['plastic', 'textile', 'leather', 'wooden'],
'action': ['stand', 'walk', 'run', 'jump', 'sit', 'lay'],
'expression': ['smile', 'cry'],
'other': ['sing', 'talk']
}
args.attr2type_dict = {value: key for key, values in args.type2attr_dict.items() for value in values}
args.type2ans_dict = json.load(open(os.path.join(args.neg_sample_dir, 'type2ans.json')))
args.ans2type_dict = {}
for type, answer_list in args.type2ans_dict.items():
if type == 'other':
continue
for answer in answer_list:
args.ans2type_dict[answer] = type
args.rel2cap = {}
args.rel2question = {}
for relation in args.all_relation_list:
if relation in {'at', 'on', 'inside of', 'interacts with', 'under'}:
args.rel2cap[relation] = '{} ' + relation + ' {}'
args.rel2question[relation] = 'is {} ' + relation + ' {}?'
elif relation in {'holds', 'wears', 'ride', 'dance', 'plays'}:
args.rel2cap[relation] = '{} ' + relation[:-1] + 'ing' + ' {}'
args.rel2question[relation] = 'is {} ' + relation[:-1] + 'ing' + ' {}?'
elif relation in {'eat', 'cut', 'hug'}:
args.rel2cap[relation] = '{} ' + relation + relation[-1] + 'ing' + ' {}'
args.rel2question[relation] = 'is {} ' + relation + relation[-1] + 'ing' + ' {}?'
elif relation in {'surf', 'hang', 'drink', 'skateboard', 'catch', 'kiss', 'throw', 'snowboard', 'kick',
'ski',
'read'}:
args.rel2cap[relation] = '{} ' + relation + 'ing' + ' {}'
args.rel2question[relation] = 'is {} ' + relation + 'ing' + ' {}?'
elif relation == 'holding hands':
args.rel2cap[relation] = '{} ' + 'holding hands with' + ' {}'
args.rel2question[relation] = 'is {} ' + 'holding hands with' + ' {}?'
elif relation == 'contain':
args.rel2cap[relation] = '{} ' + 'contains' + ' {}'
args.rel2question[relation] = 'is {} ' + 'contains' + ' {}?'
elif relation == 'talk on phone':
args.rel2cap[relation] = '{} ' + 'and' + ' {}' + ' are talking on phone'
args.rel2question[relation] = 'are {} ' + 'and' + ' {}' + ' talking on phone?'
elif relation == 'hits':
args.rel2cap[relation] = '{} ' + 'hitting' + ' {}'
args.rel2question[relation] = 'is {} ' + 'hitting' + ' {}?'
elif relation == 'highfive':
args.rel2cap[relation] = '{} ' + 'and' + ' {}' + ' are high fives'
args.rel2question[relation] = 'are {} ' + 'and' + ' {}' + ' high fives?'
elif relation == 'handshake':
args.rel2cap[relation] = '{} ' + 'and' + ' {}' + ' are shaking hands'
args.rel2question[relation] = 'are {} ' + 'and' + ' {}' + ' shaking hands?'
else:
raise NotImplementedError
elif args.task in ['caption_stage1', 'caption_stage2']:
args.train_dataset = [inputDataset(paths[i], args.selected_cols) for i in range(len(paths) - 1)]
if args.generator_version == 'fairseq':
args.generator = sequence_generator.SequenceGenerator(tokenizer=tokenizer,
beam_size=args.beam,
max_len_b=args.max_len_b,
min_len=args.min_len,
no_repeat_ngram_size=args.no_repeat_ngram_size,
constraint_range=args.constraint_range)
args.CiderD_scorer = CiderD(df=args.eval_cider_cached_tokens)
elif args.task in ['refcoco', 'refcocog', 'refcocoplus']:
args.train_dataset = [inputDataset(paths[i], args.selected_cols) for i in range(len(paths) - 1)]
if args.generator_version == 'fairseq':
args.generator = sequence_generator.SequenceGenerator(tokenizer=tokenizer,
beam_size=args.beam,
max_len_b=args.max_len_b,
min_len=args.min_len,
no_repeat_ngram_size=args.no_repeat_ngram_size,
constraint_range=args.constraint_range)
elif args.task in ['snli_ve']:
args.train_dataset = [inputDataset(paths[i], args.selected_cols) for i in range(len(paths) - 1)]
answer_item_list = []
args.index2ans = {}
args.ans2label_dict = {"no": 0, "yes": 1, "maybe": 2}
args.constraint_trie = Trie(tokenizer.eos_token_id)
for i, answer in enumerate(args.ans2label_dict.keys()):
answer_item = tokenizer(' ' + answer, return_tensors="pt",
add_special_tokens=False).input_ids.squeeze(0)
answer_item_list.append(answer_item)
args.index2ans[i] = answer
args.constraint_trie.insert([tokenizer.bos_token_id] + answer_item.tolist() + [tokenizer.eos_token_id])
constraint_mask_list = []
for answer_item in answer_item_list:
constraint_mask = torch.zeros((len(answer_item) + 1, len(args.tgt_dict))).bool()
for i in range(len(answer_item) + 1):
constraint_prefix_token = [tokenizer.bos_token_id] + answer_item[:i].tolist()
constraint_nodes = args.constraint_trie.get_next_layer(constraint_prefix_token)
constraint_mask[i][constraint_nodes] = True
constraint_mask_list.append(constraint_mask)
args.valid_answers_list = []
args.valid_constraint_masks_list = []
for i in range(0, len(answer_item_list), args.batch_size):
args.valid_answers_list += [answer_item_list[i:i + args.batch_size]]
args.valid_constraint_masks_list += [constraint_mask_list[i:i + args.batch_size]]
elif args.task in ['vqa_gen']:
args.train_dataset = [inputDataset(paths[i], args.selected_cols) for i in range(len(paths) - 1)]
if args.ans2label_file is not None:
args.ans2label_dict = pickle.load(open(args.ans2label_file, "rb"))
else:
args.ans2label_dict = {"no": 0, "yes": 1}
print("ans2label_dict", args.ans2label_dict)
answer_item_list = []
args.index2ans = {}
args.constraint_trie = Trie(tokenizer.eos_token_id)
for i, answer in enumerate(args.ans2label_dict.keys()):
answer_item = tokenizer(' ' + answer, return_tensors="pt",
add_special_tokens=False).input_ids.squeeze(0)
answer_item_list.append(answer_item)
args.index2ans[i] = answer
args.constraint_trie.insert([tokenizer.bos_token_id] + answer_item.tolist() + [tokenizer.eos_token_id])
constraint_mask_list = []
for answer_item in answer_item_list:
constraint_mask = torch.zeros((len(answer_item) + 1, len(args.tgt_dict))).bool()
for i in range(len(answer_item) + 1):
constraint_prefix_token = [tokenizer.bos_token_id] + answer_item[:i].tolist()
constraint_nodes = args.constraint_trie.get_next_layer(constraint_prefix_token)
constraint_mask[i][constraint_nodes] = True
constraint_mask_list.append(constraint_mask)
if args.val_inference_type == "allcand":
args.valid_answers_list = []
args.valid_constraint_masks_list = []
for i in range(0, len(answer_item_list), args.batch_size):
args.valid_answers_list += [answer_item_list[i:i + args.batch_size]]
args.valid_constraint_masks_list += [constraint_mask_list[i:i + args.batch_size]]
elif args.val_inference_type == "beamsearch":
args.generator = sequence_generator.SequenceGenerator(tokenizer=tokenizer,
beam_size=args.beam,
max_len_b=args.max_len_b,
min_len=args.min_len,
no_repeat_ngram_size=args.no_repeat_ngram_size,
constraint_range=args.constraint_range,
normalize_scores=False)
else:
raise NotImplementedError