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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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def func(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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def stage2_output(df_test):
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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_stage2/20230926091/checkpoint_3.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': 25}
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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 = 12
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iter = n // bsz + 1
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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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with model.maybe_autocast():
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_, _, batch_tokens = model.visual_encoder(image)
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image_embeds = model.ln_vision(batch_tokens.to(device), repr_layers=[model.vis_layers], return_contacts=True)["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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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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return_num_txt = 2
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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=3,
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max_length=30,
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repetition_penalty=5., 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, skip_special_tokens=True)
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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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with open('/cluster/home/wenkai/LAVIS/output/output_names.txt', 'a+') as f:
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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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def evaluate_score(data):
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model_config = {'arch': 'blip2_protein', 'load_finetuned': False,
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'pretrained': '/cluster/home/wenkai/LAVIS/lavis/output/BLIP2/Pretrain_stage1/20230925102/checkpoint_6.pth',
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'finetuned': '', 'num_query_token': 32, 'prompt': '',
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'model_type': 'pretrain', 'load_pretrained': True, 'freeze_vit': False,
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'max_protein_len': 512, 'max_txt_len': 30}
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model_cls = registry.get_model_class(model_config['arch'])
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model = model_cls.from_config(model_config)
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model = model.to(device)
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model.eval()
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t0 = time.time()
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proteins = list(data['protein'])
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txts = list(data['function'])
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scores = []
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for seq, txt in zip(proteins, txts):
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image = [('protein1', seq)]
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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=[30], return_contacts=True)["representations"][
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30].contiguous()
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image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
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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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use_cache=True,
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return_dict=True,
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)
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image_feats = F.normalize(model.vision_proj(query_output.last_hidden_state), dim=-1)
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image_feats_all = concat_all_gather(image_feats)
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if type(txt) == str:
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txt = eval(txt)
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length = len(txt)
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with torch.no_grad():
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text_tokens = model.tokenizer(
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txt,
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padding="max_length",
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truncation=True,
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max_length=model.max_txt_len,
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return_tensors="pt",
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).to(device)
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text_output = model.Qformer.bert(
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text_tokens.input_ids,
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attention_mask=text_tokens.attention_mask,
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return_dict=True,
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)
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text_feat = F.normalize(
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model.text_proj(text_output.last_hidden_state[:, 0, :]), dim=-1
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)
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text_feat_all = concat_all_gather(text_feat)
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sim_q2t = torch.matmul(image_feats.unsqueeze(1), text_feat_all.unsqueeze(-1)).squeeze()
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sim_i2t, _ = sim_q2t.max(-1)
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if length > 1:
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scores.append(list(sim_i2t.detach().cpu().numpy()))
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else:
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scores.append([sim_i2t.item()])
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print("model evaluate time: {}".format(time.time() - t0))
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data['sim'] = scores
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return data
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test = pd.read_csv('/cluster/home/wenkai/LAVIS/data/raw_time_split/reviewed//test.csv', sep='|')
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test['function'] = test['function'].apply(lambda x: x.lower())
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if os.path.exists('/cluster/home/wenkai/LAVIS/output/output_names.txt'):
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os.remove('/cluster/home/wenkai/LAVIS/output/output_names.txt')
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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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df_pred = pd.read_csv('/cluster/home/wenkai/LAVIS/output/output_names.txt', sep='|', header=None, on_bad_lines='warn')
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df_pred.columns = ['protein', 'function']
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df_pred = df_pred.drop_duplicates()
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df_pred['function'] = df_pred['function'].apply(lambda x: str(x).split(';'))
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df_pred['function'] = df_pred['function'].apply(lambda x: [i.strip() for i in list(set(x))])
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test.columns
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test_g = test.groupby(['protein']).agg({'function': lambda x: list(x)}).reset_index()
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test_g.columns = ['protein', 'label']
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data = pd.merge(df_pred, test_g, on='protein', how='left')
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data = data[data['label'].notnull()]
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sim = []
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for text, label in zip(data['function'].tolist(), data['label'].tolist()):
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sim.append(func(text, label))
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data['sim'] = sim
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data['avg_score'] = data['sim'].apply(lambda x: round(np.mean(x), 3))
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print("average similarity score: {}".format(round(data['avg_score'].mean(), 3)))
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data.to_csv('/cluster/home/wenkai/LAVIS/output/output_names.csv', index=False, sep='|')
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