File size: 7,314 Bytes
e740833 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
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='|')
|