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#### English scope
#device = "cuda:0"
device = "cpu"
assert device.startswith("cpu") or device.startswith("cuda")
import sys
from predict import *
from transformers import (
T5ForConditionalGeneration,
MT5ForConditionalGeneration,
ByT5Tokenizer,
PreTrainedTokenizer,
T5TokenizerFast as T5Tokenizer,
MT5TokenizerFast as MT5Tokenizer,
AutoModelForSeq2SeqLM,
AutoTokenizer,
BertTokenizer,
GPT2LMHeadModel,
)
import pandas as pd
import numpy as np
import re
from rapidfuzz import fuzz
from tqdm import tqdm
import numpy as np
from transformers import pipeline
import os
def shorten_exists(l, sim_threshold = 80, slice_size = 5):
req = []
for ele in l:
if not req:
req.append(ele)
else:
if max(map(lambda x: fuzz.ratio(x[:slice_size], ele[:slice_size]), req)) < sim_threshold:
req.append(ele)
return req
model_path = "svjack/summary-dialogue-eng"
tokenizer0 = T5Tokenizer.from_pretrained(model_path)
model0 = T5ForConditionalGeneration.from_pretrained(model_path)
if device.startswith("cuda"):
model = Obj(model0, tokenizer0, device = "cuda:0")
else:
model = Obj(model0, tokenizer0, device = "cpu")
if device.startswith("cuda"):
prompt_expand_model = pipeline('text-generation', model='daspartho/prompt-extend',
device = 0
)
else:
prompt_expand_model = pipeline('text-generation', model='daspartho/prompt-extend',
)
def loop_add(l, names = ["Tom", "Jack"]):
req = []
for i in range(len(l)):
ii = int(i % len(names))
req.append(
"{}:{}".format(names[ii], l[i])
)
return req
#### need some names drop in context(may not have ":")
#### '艾米-亚当斯在《沉睡的空洞》中，全身，双色大眼睛，咬牙切齿，恐怖，复杂的细节，电影，史诗，现实，解剖，汤姆-哈努卡，上光，艺术站，逼真，可怕'
def guess_name_candidates(context, cnt_threshold = 1):
from copy import deepcopy
assert type(context) == type("")
import re
l = re.findall(r"[\u4e00-\u9fa5a-zA-Z]+:", context)
l = list(filter(lambda x: x.strip(), l))
ori_l = deepcopy(l)
if not l:
return []
s = pd.Series(l).value_counts()
l = pd.Series(s[s > cnt_threshold].index.values.tolist()).map(lambda x: x[:-1]).values.tolist()
for ele in ori_l:
if len(ele[:-1]) not in l and (len(ele[:-1]) <= 3 or (
sum(map(len ,re.findall(r"[a-zA-Z]+:", ele))) == len(ele)
)):
l.append(ele[:-1])
l = list(set(l))
return l
def stdf_prompt_expander(x):
assert type(x) == type("")
return prompt_expand_model(x, num_return_sequences=1)[0]["generated_text"]
def simple_pred(summary, candidates = ["Tom", "Jack"], shorten_it = False,
summary_expander = lambda _:_, do_sample = True):
assert callable(summary_expander)
summary = summary_expander(summary)
pred_text = model.predict(
"{}\nCandidates:{}".format(summary, " ".join(candidates)),
do_sample = do_sample
)[0]
candidates_ = guess_name_candidates(pred_text)
l = re.split("{}".format("|".join(map(lambda x: "{}:".format(x), candidates_))) ,pred_text)
l = list(filter(lambda x: x.strip(), l))
if shorten_it:
l = shorten_exists(l)
#l = loop_add(l, candidates)
l = list(map(lambda x: x.strip(), l))
return l
def percentile_sort(df, perc_num = 101):
score_tuple_s = df["score_tuple"]
score_array = np.asarray(score_tuple_s.values.tolist())
perc_list = np.linspace(0, 100, perc_num).tolist()
low_to_high_perc_array = np.stack(list(map(lambda p: np.percentile(score_array, p, axis = 0), perc_list)))
def get_rank(array_):
lookup_list = pd.DataFrame(array_ - low_to_high_perc_array[::-1]).apply(lambda s: min(s) >= 0, axis = 1).tolist()
if True not in lookup_list:
return len(lookup_list)
return lookup_list.index(True)
rank_list = []
for i in range(score_array.shape[0]):
rank_list.append(get_rank(score_array[i, :]))
rank_s = pd.Series(rank_list)
return df.iloc[np.argsort(rank_s.values)]
def repeat_score(l, slice_size = 200 ,sim_threshold = 70):
from copy import deepcopy
assert type(l) == type([])
l = deepcopy(l)
l = sorted(l)
cnt_num = 0
set0 = set([])
for ele in l:
if ":" in ele:
ele = "".join(ele.split(":")[1:])
if set0 and max(map(lambda x: fuzz.ratio(x[:slice_size], ele[:slice_size]), set0)) > sim_threshold:
#if ele in set0:
cnt_num += 1
set0.add(ele)
return cnt_num
def sample_pred(context, times = 5, stdf_prompt_expander = lambda _: _):
df_req = []
for i in tqdm(range(times)):
ele = stdf_prompt_expander(context)
#ele = context
l = simple_pred(ele, do_sample = True)
df_req.append(
[ele, l]
)
df = pd.DataFrame(df_req)
df.columns = ["context", "dialogue"]
df["fuzz"] = df["dialogue"].map(
lambda x: fuzz.ratio(context, " ".join(x))
)
df["max_fuzz"] = df["dialogue"].map(
lambda x: max(map(lambda y: fuzz.ratio(y, context), x))
)
df["length"] = df["dialogue"].map(len)
df["rpt_score"] = df["dialogue"].map(repeat_score)
df["score_tuple"] = df.apply(
lambda x: (x["fuzz"], -1 * x["max_fuzz"], x["length"], -1 * x["rpt_score"]), axis = 1
)
df = percentile_sort(df)
return df
def sample_pred_wrapper(context, i2c_obj, times = 5, extend_by_diffusion = False):
assert type(context) == type("")
if any(map(lambda x: context.endswith(x), [".jpg", ".png", ".jpeg"])):
img_path = context
i2c_df = i2c_obj.predict_to_df([img_path])
assert i2c_df.size > 0
context = i2c_df["caption"].iloc[0]
else:
pass
assert type(context) == type("")
if extend_by_diffusion:
req_df = sample_pred(context, times = times, stdf_prompt_expander = stdf_prompt_expander)
else:
req_df = sample_pred(context, times = times, stdf_prompt_expander = lambda _: _)
return req_df
from image2caption import *
i2c_obj = Image2Caption(device = device)
if __name__ == "__main__":
from image2caption import *
i2c_obj = Image2Caption(device = device)
img_path = "../pic/bug.jpg"
img_path = "../pic/baobao.jpeg"
img_path = "../pic/cat0.jpg"
img_path = "../pic/cat.jpg"
os.path.exists(img_path)
df = sample_pred_wrapper(img_path, i2c_obj = i2c_obj)
df["dialogue"].values.tolist()
img_url = "https://datasets-server.huggingface.co/assets/metashift/--/metashift/train/2/image/image.jpg"
img_url = "https://datasets-server.huggingface.co/assets/metashift/--/metashift/train/6/image/image.jpg"
df = sample_pred_wrapper(img_url, i2c_obj = i2c_obj)
df["dialogue"].values.tolist()
text = "Goldfinger is the seventh novel in Ian Fleming's James Bond series. First published in 1959, it centres on Bond's investigation into the gold-smuggling activities of Auric Goldfinger, who is suspected of being connected to Soviet counter-intelligence. "
text
df = sample_pred_wrapper(text, i2c_obj = i2c_obj, times = 6)
df["dialogue"].values.tolist()
en_l = ['a statue of a bird on top of a rock',
'a woman standing in front of a flower arrangement',
'people walking down a dirt road',
'two pictures of a man with a beard',
'a sign that is on top of a sign',
'a woman dressed in a costume holding an umbrella',
'a woman in a red dress holding a flower in her hand',
'a little girl in a pink dress with a pink flower in her hair']
df = sample_pred(en_l[0], 5)
df["dialogue"].values.tolist()
df = sample_pred(en_l[0], 5, stdf_prompt_expander = stdf_prompt_expander)
df["dialogue"].values.tolist()
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