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| #!/usr/bin/env python3 | |
| from doctest import OutputChecker | |
| import sys | |
| import torch | |
| import re | |
| import os | |
| import gradio as gr | |
| import requests | |
| #url = "https://github.com/simonepri/lm-scorer/tree/master/lm_scorer/models" | |
| #resp = requests.get(url) | |
| 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') | |
| #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') | |
| 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 | |
| def Visual_re_ranker(caption_man, caption_woman, context_label, context_prob): | |
| caption_man = caption_man | |
| caption_woman = caption_woman | |
| context_label= context_label | |
| context_prob = 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) | |
| context_label_emb = model_sts.encode(context_label, convert_to_tensor=True) | |
| sim_m = cosine_scores = util.pytorch_cos_sim(caption_emb_man, 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, 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(context_prob))) | |
| score_woman = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(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="a man is blow drying his hair in the bathroom") , gr.Textbox(value="a woman is blow drying her hair in the bathroom"), gr.Textbox(value="hair spray"), gr.Textbox(value="0.7385")], | |
| outputs="label", | |
| ) | |
| demo.launch() | |