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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
if n > 0:
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]))
f = open('/cluster/home/wenkai/LAVIS/output/output_concat_{}{}{}.txt'.format(split, fix, type_fix), 'a+')
for i in range(len(image)):
f.write(image[i][1] + "|" + output_text_[i] + '\n')
f.close()
if __name__=="__main__":
split = 'test'
cat = ''
fix = ''
type_fix = '_pretrain'
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/pretrain/{}_sample10000.csv'.format(split),
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='|')
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