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