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import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
import torch
import numpy as np
from typing import Union, List
from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES
from .coco_zeroshot_categories import COCO_UNSEEN_CLS, COCO_SEEN_CLS, COCO_OVD_ALL_CLS, COCO_80_ALL_CLS
# https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
def whitespace_clean(text):
text = re.sub(r'\s+', ' ', text)
text = text.strip()
return text
class SimpleTokenizer(object):
def __init__(self, bpe_path: str = default_bpe()):
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
merges = merges[1:49152-256-2+1]
merges = [tuple(merge.split()) for merge in merges]
vocab = list(bytes_to_unicode().values())
vocab = vocab + [v+'</w>' for v in vocab]
self.vocab = vocab
for merge in merges:
vocab.append(''.join(merge))
vocab.extend(['<|startoftext|>', '<|endoftext|>'])
self.encoder = dict(zip(vocab, range(len(vocab))))
self.decoder = {v: k for k, v in self.encoder.items()}
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + ( token[-1] + '</w>',)
pairs = get_pairs(word)
if not pairs:
return token+'</w>'
while True:
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = ' '.join(word)
self.cache[token] = word
return word
def encode(self, text, return_link=False):
bpe_tokens = []
text = whitespace_clean(basic_clean(text)).lower()
str2id_links = [] # link original sentence word to the tokenized ids of its subwords
for token in re.findall(self.pat, text):
this_link = [token]
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
ids = [self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')]
bpe_tokens.extend(ids)
this_link.append(ids)
str2id_links.append(this_link)
if return_link:
return bpe_tokens, str2id_links
return bpe_tokens
def decode(self, tokens):
text = ''.join([self.decoder[token] for token in tokens])
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
return text
# https://github.com/openai/CLIP/blob/main/clip/clip.py
#_tokenizer = SimpleTokenizer()
def tokenize(texts: Union[str, List[str]], context_length: int = 77):
if isinstance(texts, str):
texts = [texts]
sot_token = _tokenizer.encoder["<|startoftext|>"]
eot_token = _tokenizer.encoder["<|endoftext|>"]
all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
for i, tokens in enumerate(all_tokens):
if len(tokens) > context_length:
raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
result[i, :len(tokens)] = torch.tensor(tokens)
return result
# prompt_engineering.py
def get_prompt_templates():
# prompt_templates = [
# 'There is a {} in the scene.',
# 'There is the {} in the scene.',
# 'a photo of a {} in the scene.',
# 'a photo of the {} in the scene.',
# 'a photo of one {} in the scene.',
# 'itap of a {}.',
# 'itap of my {}.', # itap: I took a picture of
# 'itap of the {}.',
# 'a photo of a {}.',
# 'a photo of my {}.',
# 'a photo of the {}.',
# 'a photo of one {}.',
# 'a photo of many {}.',
# 'a good photo of a {}.',
# 'a good photo of the {}.',
# 'a bad photo of a {}.',
# 'a bad photo of the {}.',
# 'a photo of a nice {}.',
# 'a photo of the nice {}.',
# 'a photo of a cool {}.',
# 'a photo of the cool {}.',
# 'a photo of a weird {}.',
# 'a photo of the weird {}.',
# 'a photo of a small {}.',
# 'a photo of the small {}.',
# 'a photo of a large {}.',
# 'a photo of the large {}.',
# 'a photo of a clean {}.',
# 'a photo of the clean {}.',
# 'a photo of a dirty {}.',
# 'a photo of the dirty {}.',
# 'a bright photo of a {}.',
# 'a bright photo of the {}.',
# 'a dark photo of a {}.',
# 'a dark photo of the {}.',
# 'a photo of a hard to see {}.',
# 'a photo of the hard to see {}.',
# 'a low resolution photo of a {}.',
# 'a low resolution photo of the {}.',
# 'a cropped photo of a {}.',
# 'a cropped photo of the {}.',
# 'a close-up photo of a {}.',
# 'a close-up photo of the {}.',
# 'a jpeg corrupted photo of a {}.',
# 'a jpeg corrupted photo of the {}.',
# 'a blurry photo of a {}.',
# 'a blurry photo of the {}.',
# 'a pixelated photo of a {}.',
# 'a pixelated photo of the {}.',
# 'a black and white photo of the {}.',
# 'a black and white photo of a {}.',
# 'a plastic {}.',
# 'the plastic {}.',
# 'a toy {}.',
# 'the toy {}.',
# 'a plushie {}.',
# 'the plushie {}.',
# 'a cartoon {}.',
# 'the cartoon {}.',
# 'an embroidered {}.',
# 'the embroidered {}.',
# 'a painting of the {}.',
# 'a painting of a {}.',
# ]
prompt_templates = [
'{}.',
'a photo of a {}.',
'a bad photo of a {}.',
'a photo of many {}.',
'a sculpture of a {}.',
'a photo of the hard to see {}.',
'a low resolution photo of the {}.',
'a rendering of a {}.',
'graffiti of a {}.',
'a bad photo of the {}.',
'a cropped photo of the {}.',
'a tattoo of a {}.',
'the embroidered {}.',
'a photo of a hard to see {}.',
'a bright photo of a {}.',
'a photo of a clean {}.',
'a photo of a dirty {}.',
'a dark photo of the {}.',
'a drawing of a {}.',
'a photo of my {}.',
'the plastic {}.',
'a photo of the cool {}.',
'a close-up photo of a {}.',
'a black and white photo of the {}.',
'a painting of the {}.',
'a painting of a {}.',
'a pixelated photo of the {}.',
'a sculpture of the {}.',
'a bright photo of the {}.',
'a cropped photo of a {}.',
'a plastic {}.',
'a photo of the dirty {}.',
'a jpeg corrupted photo of a {}.',
'a blurry photo of the {}.',
'a photo of the {}.',
'a good photo of the {}.',
'a rendering of the {}.',
'a {} in a video game.',
'a photo of one {}.',
'a doodle of a {}.',
'a close-up photo of the {}.',
'the origami {}.',
'the {} in a video game.',
'a sketch of a {}.',
'a doodle of the {}.',
'a origami {}.',
'a low resolution photo of a {}.',
'the toy {}.',
'a rendition of the {}.',
'a photo of the clean {}.',
'a photo of a large {}.',
'a rendition of a {}.',
'a photo of a nice {}.',
'a photo of a weird {}.',
'a blurry photo of a {}.',
'a cartoon {}.',
'art of a {}.',
'a sketch of the {}.',
'a embroidered {}.',
'a pixelated photo of a {}.',
'itap of the {}.',
'a jpeg corrupted photo of the {}.',
'a good photo of a {}.',
'a plushie {}.',
'a photo of the nice {}.',
'a photo of the small {}.',
'a photo of the weird {}.',
'the cartoon {}.',
'art of the {}.',
'a drawing of the {}.',
'a photo of the large {}.',
'a black and white photo of a {}.',
'the plushie {}.',
'a dark photo of a {}.',
'itap of a {}.',
'graffiti of the {}.',
'a toy {}.',
'itap of my {}.',
'a photo of a cool {}.',
'a photo of a small {}.',
'a tattoo of the {}.',
]
return prompt_templates
def prompt_engineering(classnames, template=""):
return template.replace('{}', classnames.replace(',', '').replace('+', ' '))
# clip_img_tsv.py
def convert_example_to_features_bpe(text, tokenizer, sot_token, eot_token, context_length=77):
"""
Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample.
:param tokenizer: Tokenizer
:return: List, a list containing token id, padded by 0
"""
assert isinstance(text, str)
input_ids = [sot_token] + tokenizer.encode(text) + [eot_token]
if len(input_ids) > context_length:
input_ids = input_ids[:context_length]
input_ids = np.array(input_ids)
pad_input_ids = np.zeros(context_length)
pad_input_ids[:input_ids.shape[0]] = input_ids
return pad_input_ids
def get_cls_names(filter_novel=False, coco=None, from_file=False):
""" return a list of strings with each string as name of a class
"""
# the names are stored in a txt file
if from_file:
# coco_det_cls = {COCO_80_ALL_CLS[key]: key for key in COCO_80_ALL_CLS}
# # not found in nouns {'skis': 31, 'sports ball': 33, 'hot dog': 53, 'potted plant': 59, 'scissors': 77, 'hair drier': 79}
# coco_det_cls['ski'] = 81
# coco_det_cls['scissor'] = 82
# with open('/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/COCO_Caption_nouns_4688.txt','w') as g:
# with open(from_file, 'r') as f:
# cnt = 0
# for row in f:
# if row.split(",")[0] not in coco_det_cls:
# g.write(row)
# cnt += 1
# else:
# coco_det_cls.pop(row.split(",")[0])
names = []
with open(from_file, 'r') as f:
for row in f:
names.append(row.split(",")[0])
return names
# classes' names
if coco == 'target':
return COCO_UNSEEN_CLS
elif coco == 'base':
return COCO_SEEN_CLS
elif coco == 'all':
return COCO_OVD_ALL_CLS
elif coco == 'all_80':
return [COCO_80_ALL_CLS[i+1] for i in range(80)]
assert len(LVIS_V1_CATEGORIES) == 1203
cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES]
assert min(cat_ids) == 1 and max(cat_ids) == len(
cat_ids
), "Category ids are not in [1, #categories], as expected"
# Ensure that the category list is sorted by id
lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"])
if filter_novel:
class_names = [cls_meta['name'] for cls_meta in lvis_categories if cls_meta['frequency'] != 'r']
else:
class_names = [cls_meta['name'] for cls_meta in lvis_categories]
# remove or replace special symbols
class_names = [cls_n.replace("_", " ") for cls_n in class_names]
class_names = [cls_n.replace("(", "") for cls_n in class_names]
class_names = [cls_n.replace(")", "") for cls_n in class_names]
return class_names
def pre_tokenize(class_names):
"""
pre-tokenize class names
:param class_names: List, a list of class names
:param tokenizer: Tokenizer, SimpleTokenizer()
:return: Tensor, containing all prompts for all classes, [#cls, #prompts, context_length]
"""
# tokenizer
tokenizer = SimpleTokenizer()
sot_token = tokenizer.encoder["<|startoftext|>"]
eot_token = tokenizer.encoder["<|endoftext|>"]
# prompt engineering
prompt_templates = get_prompt_templates()
input_ids_all = []
for k in range(len(class_names)):
v = class_names[k]
if isinstance(v, str):
vs = [v]
elif isinstance(v, list):
vs = v
t1s = []
for v in vs:
for pt in prompt_templates:
t1s.append(prompt_engineering(v, template=pt))
input_ids = []
for t1 in t1s:
this_input_ids = convert_example_to_features_bpe(t1, tokenizer, sot_token, eot_token)
input_ids.append(torch.tensor(this_input_ids, dtype=torch.long))
input_ids_all.append(torch.stack(input_ids, 0))
input_ids_all_classes = torch.stack(input_ids_all, 0)
return input_ids_all_classes
if __name__ == "__main__":
flatten_input_ids = pre_tokenize()
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