import os import re import torch from PIL import Image from lavis.models import load_model_and_preprocess from lavis.processors import load_processor from lavis.common.registry import registry from torch.nn import functional as F from lavis.models.base_model import all_gather_with_grad, concat_all_gather import numpy as np import pandas as pd import time from fuzzywuzzy import process from multiprocessing import Pool, Queue, Process import difflib import Levenshtein # import obonet # setup device to use device = torch.device("cuda") if torch.cuda.is_available() else "cpu" # device = torch.device("cuda") def txt_map(x, txt_dict): if type(x) == str: x = eval(x) x_ = [] for i in x: if i in txt_dict: x_.append(txt_dict[i]) else: x_.append(i) return x_ def levenshtein_sim(text, label): all_s = [] for x in label: s = 0 for y in text: temp = Levenshtein.ratio(x, y) if temp > s: s = temp all_s.append(s) all_s = [round(i, 3) for i in all_s] return all_s def func(text, label): all_s = [] for x in text: s = 0 for y in label: temp = Levenshtein.ratio(x, y) if temp > s: s = temp all_s.append(s) all_s = [round(i, 3) for i in all_s] return all_s def stage2_output(df_test, return_num_txt=1): config = {'arch': 'blip2_protein_opt', 'load_finetuned': False, 'pretrained': '/cluster/home/wenkai/LAVIS/lavis/output/BLIP2/Pretrain_stage2/20231029182/checkpoint_0.pth', 'finetuned': '', 'num_query_token': 32, 'opt_model': 'facebook/opt-2.7b', 'prompt': '', 'model_type': 'pretrain_protein_opt2.7b', 'load_pretrained': True, 'freeze_vit': True, 'max_protein_len': 600, 'max_txt_len': 256} model_cls = registry.get_model_class(config['arch']) model = model_cls.from_config(config) model.to(device) model.eval() images = df_test['protein'].tolist() n = len(images) bsz = 8 iter = n // bsz + 1 with open('/cluster/home/wenkai/LAVIS/output/output_concat_{}{}{}.txt'.format(split, fix, type_fix), 'a+') as f: for i in range(iter): image = images[i * bsz: min(n, (i + 1) * bsz)] image = [('protein{}'.format(i), x) for i, x in enumerate(image)] with model.maybe_autocast(): _, _, batch_tokens = model.visual_encoder(image) image_embeds = \ model.ln_vision(batch_tokens.to(device), repr_layers=[model.vis_layers], return_contacts=True)[ "representations"][model.vis_layers].contiguous() image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device) query_tokens = model.query_tokens.expand(image_embeds.shape[0], -1, -1) query_output = model.Qformer.bert( query_embeds=query_tokens, encoder_hidden_states=image_embeds, encoder_attention_mask=image_atts, return_dict=True, ) inputs_opt = model.opt_proj(query_output.last_hidden_state) atts_opt = torch.ones(inputs_opt.size()[:-1], dtype=torch.long).to(device) model.opt_tokenizer.padding_side = "right" text = ['' for i in range(len(image))] opt_tokens = model.opt_tokenizer( text, return_tensors="pt", padding="longest", truncation=True, max_length=model.max_txt_len, ).to(device) inputs_embeds = model.opt_model.model.decoder.embed_tokens(opt_tokens.input_ids) inputs_embeds = torch.cat([inputs_opt, inputs_embeds], dim=1) attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) num_txt = 5 with model.maybe_autocast(): outputs = model.opt_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_mask, min_length=1, max_length=256, repetition_penalty=1., num_beams=num_txt, eos_token_id=50118, length_penalty=1., num_return_sequences=return_num_txt, temperature=1.) output_text = model.opt_tokenizer.batch_decode(outputs) output_text = [re.sub('\t', '', str(x)) for x in output_text] output_text = [text.strip() for text in output_text] output_text_ = [] for i in range(len(image)): output_text_.append(';'.join(output_text[i * return_num_txt:(i + 1) * return_num_txt])) for i in range(len(image)): f.write(image[i][1] + "|" + output_text_[i] + '\n') if __name__=="__main__": split = 'test' cat = 'bp' fix = '_mf' type_fix = '' if cat == 'bp': fix = '_bp' if cat == 'cc': fix = '_cc' print(device) return_num_txt = 1 # graph = obonet.read_obo("http://purl.obolibrary.org/obo/go.obo") ### Levenshtein similarity print("reading file ...") test = pd.read_csv('/cluster/home/wenkai/LAVIS/data/sim_split_concat/{}{}.csv'.format(split, fix), usecols=['name', 'protein', 'function'], sep='|') # test['function'] = test['function'].apply(lambda x: x.lower().split('; ')) test.columns = ['name', 'protein', 'label'] if os.path.exists('/cluster/home/wenkai/LAVIS/output/output_concat_{}{}{}.txt'.format(split, fix, type_fix)): os.remove('/cluster/home/wenkai/LAVIS/output/output_concat_{}{}{}.txt'.format(split, fix, type_fix)) print("stage 2 predict starting") stage2_output(test) print("stage 2 predict completed") df_pred = pd.read_csv('/cluster/home/wenkai/LAVIS/output/output_concat_{}{}{}.txt'.format(split, fix, type_fix), sep='|', header=None, on_bad_lines='warn') df_pred.columns = ['protein', 'pred'] df_pred = df_pred.drop_duplicates() # df_pred['function'] = df_pred['function'].apply(lambda x: str(x).split(';')) # df_pred['function'] = df_pred['function'].apply(lambda x: [i.strip() for i in list(set(x))]) data = pd.merge(df_pred, test, on='protein', how='left') data = data[data['label'].notnull()] # sim = [] # for text, label in zip(data['function'].tolist(), data['label'].tolist()): # sim.append(func(text, label)) # data['sim'] = sim # data['avg_score'] = data['sim'].apply(lambda x: round(np.mean(x), 3)) # data['count'] = data['sim'].apply(lambda x: x.count(1.)) # print("average similarity score: {}".format(round(data['avg_score'].mean(), 3))) # print("Return texts: {}; Accuracy: {}".format(return_num_txt, data['count'].sum()/(return_num_txt*data.shape[0]))) data[['name', 'label', 'pred']].to_csv( '/cluster/home/wenkai/LAVIS/output/predict_concat_{}{}{}.csv'.format(split, cat, type_fix), index=False, sep='|')