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import os
import random
from typing import List
import torch
def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
"""construct a map such that positive_map[i,j] = True iff box i is associated to token j
Input:
- tokenized:
- input_ids: Tensor[1, ntokens]
- attention_mask: Tensor[1, ntokens]
- token_span: list with length num_boxes.
- each item: [start_idx, end_idx]
"""
positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
for j, tok_list in enumerate(token_span):
for (beg, end) in tok_list:
beg_pos = tokenized.char_to_token(beg)
end_pos = tokenized.char_to_token(end - 1)
if beg_pos is None:
try:
beg_pos = tokenized.char_to_token(beg + 1)
if beg_pos is None:
beg_pos = tokenized.char_to_token(beg + 2)
except:
beg_pos = None
if end_pos is None:
try:
end_pos = tokenized.char_to_token(end - 2)
if end_pos is None:
end_pos = tokenized.char_to_token(end - 3)
except:
end_pos = None
if beg_pos is None or end_pos is None:
continue
assert beg_pos is not None and end_pos is not None
if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE":
positive_map[j, beg_pos] = 1
break
else:
positive_map[j, beg_pos : end_pos + 1].fill_(1)
return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
def build_captions_and_token_span(cat_list, force_lowercase):
"""
Return:
captions: str
cat2tokenspan: dict
{
'dog': [[0, 2]],
...
}
"""
cat2tokenspan = {}
captions = ""
for catname in cat_list:
class_name = catname
if force_lowercase:
class_name = class_name.lower()
if "/" in class_name:
class_name_list: List = class_name.strip().split("/")
class_name_list.append(class_name)
class_name: str = random.choice(class_name_list)
tokens_positive_i = []
subnamelist = [i.strip() for i in class_name.strip().split(" ")]
for subname in subnamelist:
if len(subname) == 0:
continue
if len(captions) > 0:
captions = captions + " "
strat_idx = len(captions)
end_idx = strat_idx + len(subname)
tokens_positive_i.append([strat_idx, end_idx])
captions = captions + subname
if len(tokens_positive_i) > 0:
captions = captions + " ."
cat2tokenspan[class_name] = tokens_positive_i
return captions, cat2tokenspan
def build_id2posspan_and_caption(category_dict: dict):
"""Build id2pos_span and caption from category_dict
Args:
category_dict (dict): category_dict
"""
cat_list = [item["name"].lower() for item in category_dict]
id2catname = {item["id"]: item["name"].lower() for item in category_dict}
caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)
id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}
return id2posspan, caption
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