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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 | |
| import torch | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| from torch.nn.functional import softmax | |
| import numpy as np | |
| # just for the sake of this demo, we use cloze prob to initialize the hypothesis | |
| #url = "https://github.com/simonepri/lm-scorer/tree/master/lm_scorer/models" | |
| #resp = requests.get(url) | |
| from sentence_transformers import SentenceTransformer, util | |
| 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 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('distilgpt2') | |
| #tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2') | |
| tokenizer = GPT2Tokenizer.from_pretrained('gpt2') | |
| model = GPT2LMHeadModel.from_pretrained('gpt2') | |
| def sentence_prob_mean(text): | |
| # Tokenize the input text and add special tokens | |
| input_ids = tokenizer.encode(text, return_tensors='pt') | |
| # Obtain model outputs | |
| with torch.no_grad(): | |
| outputs = model(input_ids, labels=input_ids) | |
| logits = outputs.logits # logits are the model outputs before applying softmax | |
| # Shift logits and labels so that tokens are aligned: | |
| shift_logits = logits[..., :-1, :].contiguous() | |
| shift_labels = input_ids[..., 1:].contiguous() | |
| # Calculate the softmax probabilities | |
| probs = softmax(shift_logits, dim=-1) | |
| # Gather the probabilities of the actual token IDs | |
| gathered_probs = torch.gather(probs, 2, shift_labels.unsqueeze(-1)).squeeze(-1) | |
| # Compute the mean probability across the tokens | |
| mean_prob = torch.mean(gathered_probs).item() | |
| return mean_prob | |
| def cos_sim(a, b): | |
| return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b))) | |
| def Visual_re_ranker(caption_man, caption_woman, visual_context_label, context_prob): | |
| caption_man = caption_man | |
| caption_woman = caption_woman | |
| visual_context_label = visual_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(visual_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 = sentence_prob_mean(caption_man) | |
| LM_woman = sentence_prob_mean(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 (distilbert)", | |
| inputs=[gr.Textbox(value="a man riding a motorcycle on a road") , gr.Textbox(value="a woman riding a motorcycle on a road"), gr.Textbox(value="motor scooter"), gr.Textbox(value="0.2183")], | |
| outputs="label", | |
| ) | |
| demo.launch() | |