File size: 6,520 Bytes
bc64314 713f7a2 bc64314 daa509a bc64314 daa509a bc64314 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
#!/usr/bin/env python3
from doctest import OutputChecker
import sys
#import torch
import re
import os
import gradio as gr
import requests
from sentence_transformers import SentenceTransformer, util
#url = "https://github.com/simonepri/lm-scorer/tree/master/lm_scorer/models"
#resp = requests.get(url)
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from transformers import T5Tokenizer, AutoModelForCausalLM
from arabert import ArabertPreprocessor
from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel
from transformers import AutoTokenizer, AutoModel
from arabert.preprocess import ArabertPreprocessor
from sentence_transformers import SentenceTransformer, util
#from sentence_transformers import SentenceTransformer, util
#from sklearn.metrics.pairwise import cosine_similarity
#from lm_scorer.models.auto import AutoLMScorer as LMScorer
#from sentence_transformers import SentenceTransformer, util
#from sklearn.metrics.pairwise import cosine_similarity
#model_sts = gr.Interface.load('huggingface/sentence-transformers/stsb-distilbert-base')
#model_sts = SentenceTransformer('stsb-distilbert-base')
#model_sts = SentenceTransformer('roberta-large-nli-stsb-mean-tokens')
model_sts = SentenceTransformer('distiluse-base-multilingual-cased-v1')
#batch_size = 1
#scorer = LMScorer.from_pretrained('gpt2' , device=device, batch_size=batch_size)
#import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import numpy as np
import re
def Sort_Tuple(tup):
# (Sorts in descending order)
tup.sort(key = lambda x: x[1])
return tup[::-1]
def softmax(x):
exps = np.exp(x)
return np.divide(exps, np.sum(exps))
def get_sim(x):
x = str(x)[1:-1]
x = str(x)[1:-1]
return x
# Load pre-trained model
#model = GPT2LMHeadModel.from_pretrained('distilgpt2', output_hidden_states = True, output_attentions = True)
model = GPT2LMHeadModel.from_pretrained('gpt2', output_hidden_states = True, output_attentions = True)
#model = gr.Interface.load('huggingface/distilgpt2', output_hidden_states = True, output_attentions = True)
#model.eval()
#tokenizer = gr.Interface.load('huggingface/distilgpt2')
#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
model_name = "aubmindlab/aragpt2-base"
arabert_prep = ArabertPreprocessor(model_name=model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name, output_hidden_states=True, output_attentions=True)
tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
#model.eval()
def cloze_prob(text):
whole_text_encoding = tokenizer.encode(text)
# Parse out the stem of the whole sentence (i.e., the part leading up to but not including the critical word)
text_list = text.split()
stem = ' '.join(text_list[:-1])
stem_encoding = tokenizer.encode(stem)
# cw_encoding is just the difference between whole_text_encoding and stem_encoding
# note: this might not correspond exactly to the word itself
cw_encoding = whole_text_encoding[len(stem_encoding):]
# Run the entire sentence through the model. Then go "back in time" to look at what the model predicted for each token, starting at the stem.
# Put the whole text encoding into a tensor, and get the model's comprehensive output
tokens_tensor = torch.tensor([whole_text_encoding])
with torch.no_grad():
outputs = model(tokens_tensor)
predictions = outputs[0]
logprobs = []
# start at the stem and get downstream probabilities incrementally from the model(see above)
start = -1-len(cw_encoding)
for j in range(start,-1,1):
raw_output = []
for i in predictions[-1][j]:
raw_output.append(i.item())
logprobs.append(np.log(softmax(raw_output)))
# if the critical word is three tokens long, the raw_probabilities should look something like this:
# [ [0.412, 0.001, ... ] ,[0.213, 0.004, ...], [0.002,0.001, 0.93 ...]]
# Then for the i'th token we want to find its associated probability
# this is just: raw_probabilities[i][token_index]
conditional_probs = []
for cw,prob in zip(cw_encoding,logprobs):
conditional_probs.append(prob[cw])
# now that you have all the relevant probabilities, return their product.
# This is the probability of the critical word given the context before it.
return np.exp(np.sum(conditional_probs))
def cos_sim(a, b):
return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b)))
#def Visual_re_ranker(caption, visual_context_label, visual_context_prob):
def Visual_re_ranker(caption_man, caption_woman, visual_context_label, visual_context_prob):
caption_man = caption_man
caption_woman = caption_woman
visual_context_label= visual_context_label
visual_context_prob = visual_context_prob
caption_emb_man = model_sts.encode(caption_man, convert_to_tensor=True)
caption_emb_woman = model_sts.encode(caption_woman, convert_to_tensor=True)
visual_context_label_emb = model_sts.encode(visual_context_label, convert_to_tensor=True)
sim_m = cosine_scores = util.pytorch_cos_sim(caption_emb_man, visual_context_label_emb)
sim_m = sim_m.cpu().numpy()
sim_m = get_sim(sim_m)
sim_w = cosine_scores = util.pytorch_cos_sim(caption_emb_woman, visual_context_label_emb)
sim_w = sim_w.cpu().numpy()
sim_w = get_sim(sim_w)
LM_man = cloze_prob(caption_man)
LM_woman = cloze_prob(caption_woman)
#LM = scorer.sentence_score(caption, reduce="mean")
score_man = pow(float(LM_man),pow((1-float(sim_m))/(1+ float(sim_m)),1-float(visual_context_prob)))
score_woman = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(visual_context_prob)))
#return {"LM": float(LM)/1, "sim": float(sim)/1, "score": float(score)/1 }
return {"Man": float(score_man)/1, "Woman": float(score_woman)/1}
#return LM, sim, score
demo = gr.Interface(
fn=Visual_re_ranker,
description="Demo for Women Wearing Lipstick: Measuring the Bias Between Object and Its Related Gender",
#inputs=[gr.Textbox(value="a man sitting on a surfboard in the ocean") , gr.Textbox(value="a woman sitting on a surfboard in the ocean"), gr.Textbox(value="paddle"), gr.Textbox(value="0.5283")],
inputs=[gr.Textbox(value="شعرها جميل اليوم") , gr.Textbox(value="شعره جميل اليوم"), gr.Textbox(value="جميل"), gr.Textbox(value="0.7385")],
outputs="label",
)
demo.launch()
|