examples/blip2_predict_func_concat_pretrain.py

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='|')