Delete run.py

run.py

@@ -1,101 +0,0 @@
-import os
-import pandas as pd
-
-import torch
-from torch.nn import functional as F
-from transformers import AutoTokenizer
-
-from util.utils import *
-
-from tqdm import tqdm
-from train import markerModel
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
-
-device_count = torch.cuda.device_count()
-device_biomarker = torch.device('cuda' if torch.cuda.is_available() else "cpu")
-
-device = torch.device('cpu')
-d_model_name = 'DeepChem/ChemBERTa-10M-MTR'
-p_model_name = 'DeepChem/ChemBERTa-10M-MLM'
-
-tokenizer = AutoTokenizer.from_pretrained(d_model_name)
-prot_tokenizer = AutoTokenizer.from_pretrained(p_model_name)
-
-#--biomarker Model
-##-- hyper param config file Load --##
-config = load_hparams('config/predict.json')
-config = DictX(config)
-model = markerModel.load_from_checkpoint(config.load_checkpoint,strict=False)
-        
-# model = BiomarkerModel.load_from_checkpoint('./biomarker_bindingdb_train8595_pretopre/3477h3wf/checkpoints/epoch=30-step=7284.ckpt').to(device_biomarker)
-
-model.eval()
-model.freeze()
-    
-if device_biomarker.type == 'cuda':
-    model = torch.nn.DataParallel(model)
-    
-def get_biomarker(drug_inputs, prot_inputs):
-    output_preds = model(drug_inputs, prot_inputs)
-    
-    predict = torch.squeeze((output_preds)).tolist()
-
-    # output_preds = torch.relu(output_preds)
-    # predict = torch.tanh(output_preds)
-    # predict = predict.squeeze(dim=1).tolist()
-
-    return predict
-
-
-def biomarker_prediction(smile_acc, smile_don):
-    try:
-        aas_input = smile_acc
-       
-            
-        das_input =smile_don
-        d_inputs = tokenizer(aas_input, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
-        # d_inputs = tokenizer(smiles, truncation=True, return_tensors="pt")
-        drug_input_ids = d_inputs['input_ids'].to(device)
-        drug_attention_mask = d_inputs['attention_mask'].to(device)
-        drug_inputs = {'input_ids': drug_input_ids, 'attention_mask': drug_attention_mask}
-
-        p_inputs = prot_tokenizer(das_input, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
-        # p_inputs = prot_tokenizer(aas_input, truncation=True, return_tensors="pt")
-        prot_input_ids = p_inputs['input_ids'].to(device)
-        prot_attention_mask = p_inputs['attention_mask'].to(device)
-        prot_inputs = {'input_ids': prot_input_ids, 'attention_mask': prot_attention_mask}
-
-        output_predict = get_biomarker(drug_inputs, prot_inputs)
-        
-        return output_predict
-
-    except Exception as e:
-        print(e)
-        return {'Error_message': e}
-
-
-def smiles_aas_test(smile_acc,smile_don):
-
-    batch_size = 1
-    try:
-        output_pred = biomarker_prediction((smile_acc), (smile_don))
-
-        datas = output_pred
-
-        ## -- Export result data to csv -- ##
-        # df = pd.DataFrame(datas)
-        # df.to_csv('./results/predict_test.csv', index=None)
-
-        # print(df)
-        return datas
-        
-    except Exception as e:
-        print(e)
-        return {'Error_message': e}
-    
-
-
-
-                             
-