add mol_test

dataset.py

@@ -260,6 +260,23 @@ def get_task(task_name):
                             }
 
         return df_train, tokenizer_config
+    elif task_name.lower() == 'test_mol':
+        df_test = {"sps": './data/test/test_sps',
+                    'seq': './data/test/test_protein_seq',
+                    "smile": './data/test/test_smile',
+                    "affinity": './data/test/test_ic50',
+                    }
+
+        tokenizer_config = {"vocab_file": './config/vocab_mol.txt',
+                            "vocab_pair": './config/drug_codes_chembl.txt',
+                            "vocab_pair_p": './config/protein_codes_uniprot.txt',
+                            "begin_id": '[CLS]',
+                            "separate_id": "[SEP]",
+                            "max_len": 595
+                            }
+
+        return df_test, tokenizer_config
+
 
 
 if __name__ == "__main__":

test.py

-from transformers import BertTokenizer
-from modeling_bert import BertForMaskedLM
-import torch
-import numpy as np
+from PIL import Image
+import os
 
-a = np.ones((5))
+img_1_path = "E:\FIVN论文\照片\朱学凯.jpeg"
+filename = "E:\FIVN论文\照片\朱学凯_gray.jpeg"
 
-b = a[:4]
-print('----------------')
+def RGBtoGray(path, save):
+    # files = os.listdir(path)
\ No newline at end of file
+    # for file in files:
+    #     imgpath = path + '/' + file
+    #     #print(imgpath)
+        #
+    img = Image.open(path).convert('L')
+        #resize将图像像素转换成自己需要的像素大小
+        # img = im.resize((512, 512))
+        # dirpath = filename
+        # file_name, file_extend = os.path.splitext(f)
+        # dst = os.path.join(os.path.abspath(dirpath), file_name + '.jpg')
+    img.save(save)
+if __name__ == "__main__":
+    RGBtoGray(img_1_path, filename)