| #!/usr/bin/env python3 | |
| from doctest import OutputChecker | |
| import sys | |
| import torch | |
| import re | |
| import os | |
| import gradio as gr | |
| import requests | |
| 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 | |
| #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 | |
| #device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| #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('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('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)) | |
| 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_G, caption_B, caption_VR, visual_context_label, visual_context_prob): | |
| caption_G = caption_G | |
| caption_B = caption_B | |
| caption_VR = caption_VR | |
| visual_context_label= visual_context_label | |
| visual_context_prob = visual_context_prob | |
| caption_emb_G = model_sts.encode(caption_G, convert_to_tensor=True) | |
| caption_emb_B = model_sts.encode(caption_B, convert_to_tensor=True) | |
| caption_emb_VR = model_sts.encode(caption_VR, convert_to_tensor=True) | |
| visual_context_label_emb = model_sts.encode(visual_context_label, convert_to_tensor=True) | |
| sim_1 = cosine_scores = util.pytorch_cos_sim(caption_emb_G, visual_context_label_emb) | |
| sim_1 = sim_1.cpu().numpy() | |
| sim_1 = get_sim(sim_1) | |
| sim_2 = cosine_scores = util.pytorch_cos_sim(caption_emb_B, visual_context_label_emb) | |
| sim_2 = sim_2.cpu().numpy() | |
| sim_2 = get_sim(sim_2) | |
| sim_3 = cosine_scores = util.pytorch_cos_sim(caption_emb_VR, visual_context_label_emb) | |
| sim_3 = sim_3.cpu().numpy() | |
| sim_3 = get_sim(sim_3) | |
| LM_1 = sentence_prob_mean(caption_G) | |
| LM_2 = sentence_prob_mean(caption_B) | |
| LM_3 = sentence_prob_mean(caption_VR) | |
| #LM = scorer.sentence_score(caption, reduce="mean") | |
| score_1 = pow(float(LM_1),pow((1-float(sim_1))/(1+ float(sim_1)),1-float(visual_context_prob))) | |
| score_2 = pow(float(LM_2),pow((1-float(sim_2))/(1+ float(sim_2)),1-float(visual_context_prob))) | |
| score_3 = pow(float(LM_3),pow((1-float(sim_3))/(1+ float(sim_3)),1-float(visual_context_prob))) | |
| #return {"LM": float(LM)/1, "sim": float(sim)/1, "score": float(score)/1 } | |
| return {"Greedy": float(score_1)/1, "Best-Beam-5": float(score_2)/1, "Visual_re-Ranker": float(score_3)/1 } | |
| #return LM, sim, score | |
| demo = gr.Interface( | |
| fn=Visual_re_ranker, | |
| #description="Demo for Belief Revision based Caption Re-ranker with Visual Semantic Information", | |
| description="Demo for Caption Re-ranker with Visual Semantic Information", | |
| #inputs=[gr.Textbox(value="a city street filled with traffic at night") , gr.Textbox(value="traffic"), gr.Textbox(value="0.7458009")], | |
| # a baby is eating in front of a birthday cake /a baby sitting in front of a giant cake | |
| inputs=[gr.Textbox(value="baby is eating in front of a birthday cake") , gr.Textbox(value="a baby sitting in front of a cake"), gr.Textbox(value="a baby sitting in front of a birthday cake"), gr.Textbox(value="candle wax light"), gr.Textbox(value="0.958")], | |
| #outputs=[gr.Textbox(value="Language Model Score") , gr.Textbox(value="Semantic Similarity Score"), gr.Textbox(value="Belief revision score via visual context")], | |
| outputs="label", | |
| ) | |
| demo.launch() |