Delete FAPM_inference.py

FAPM_inference.py

@@ -1,76 +0,0 @@
-import torch
-import torch.nn as nn
-import pandas as pd
-import torch.nn.functional as F
-from lavis.models.protein_models.protein_function_opt import Blip2ProteinMistral
-# from lavis.models.base_model import FAPMConfig
-# from lavis.models.blip2_models.blip2_opt import Blip2ProteinOPT
-import random
-from lavis.models.base_model import FAPMConfig
-
-prop = True
-
-# model = Blip2ProteinOPT(config=FAPMConfig(), esm_size='3b')
-# model.load_checkpoint('/cluster/home/wenkai/LAVIS/lavis/output/BLIP2/Pretrain_stage2/20240327081/checkpoint_2.pth')
-model = Blip2ProteinMistral(config=FAPMConfig(), esm_size='3b')
-model.load_checkpoint('model/checkpoint_mf2.pth')
-# model.from_pretrained('/cluster/home/wenkai/FAPM_model/mf')
-model.to('cuda')
-
-# esm_emb = torch.load('/cluster/home/wenkai/LAVIS/data/pretrain/ipr_domain_emb_esm2_3b/Gp49.pt')['representations'][36]
-esm_emb = torch.load('data/emb_esm2_3b/P18281.pt')['representations'][36]
-esm_emb = F.pad(esm_emb.t(), (0, 1024 - len(esm_emb))).t().to('cuda')
-samples = {'name': ['P18281'],
-           'image': torch.unsqueeze(esm_emb, dim=0),
-           'text_input': ['actin monomer binding'],
-           'prompt': ['Acanthamoeba']}
-prediction = model.generate(samples, length_penalty=0., num_beams=15, num_captions=10, temperature=1., repetition_penalty=1.0)
-print(f"Text Prediction: {prediction}")
-
-
-if prop == True:
-    from data.evaluate_data.utils import Ontology
-    import difflib
-    import re
-
-    # godb = Ontology(f'/cluster/home/wenkai/LAVIS/data/go1.4-basic.obo', with_rels=True)
-    godb = Ontology(f'data/go1.4-basic.obo', with_rels=True)
-
-    go_des = pd.read_csv('data/go_descriptions1.4.txt', sep='|', header=None)
-    go_des.columns = ['id', 'text']
-    go_des = go_des.dropna()
-    go_des['id'] = go_des['id'].apply(lambda x: re.sub('_', ':', x))
-    go_obo_set = set(go_des['id'].tolist())
-    go_des['text'] = go_des['text'].apply(lambda x: x.lower())
-    GO_dict = dict(zip(go_des['text'], go_des['id']))
-    Func_dict = dict(zip(go_des['id'], go_des['text']))
-
-    # terms_mf = pd.read_pickle('/cluster/home/wenkai/deepgo2/data/mf/terms.pkl')
-    terms_mf = pd.read_pickle('data/terms/mf_terms.pkl')
-    choices_mf = [Func_dict[i] for i in list(set(terms_mf['gos']))]
-    choices = {x.lower(): x for x in choices_mf}
-
-    pred_terms_list = []
-    pred_go_list = []
-    prop_annotations = []
-    for x in prediction:
-        x = [eval(i) for i in x.split('; ')]
-        pred_terms = []
-        pred_go = []
-        annot_set = set()
-        for i in x:
-            txt = i[0]
-            prob = i[1]
-            sim_list = difflib.get_close_matches(txt.lower(), choices, n=1, cutoff=0.9)
-            if len(sim_list) > 0:
-                pred_terms.append((sim_list[0], prob))
-                pred_go.append((GO_dict[sim_list[0]], prob))
-                annot_set |= godb.get_anchestors(GO_dict[sim_list[0]])
-        pred_terms_list.append(pred_terms)
-        pred_go_list.append(pred_go)
-        annots = list(annot_set)
-        prop_annotations.append(annots)
-
-    print(f"Predictions of GO terms: \n{pred_terms_list} \nPredictions of GO id: \n{pred_go_list} \nPredictions of GO id propgated: \n{prop_annotations}")
-
-