change test

dataset.py

@@ -140,11 +140,13 @@ class Data_Encoder(data.Dataset):
 
         input_seq = [self.begin_id] + d + [self.sep_id] + p + [self.sep_id]
         token_type_ids = np.concatenate((np.zeros((len(d) + 2), dtype=np.int), np.ones((len(p) + 1), dtype=np.int)))
-        token_type_ids = np.pad(token_type_ids, (0, self.max_len-len(input_seq)), 'constant', constant_values=0)
+        token_type_ids = np.pad(token_type_ids, (0, self.max_len - len(input_seq)), 'constant', constant_values=0)
         input, input_mask = seq2emb_encoder(input_seq, self.max_len, self.vocab)
-        return torch.from_numpy(input).long(), torch.from_numpy(token_type_ids).long(), torch.from_numpy(input_mask).long(), y
+        return torch.from_numpy(input).long(), torch.from_numpy(token_type_ids).long(), torch.from_numpy(
+            input_mask).long(), y
         # return len(d), len(p)
 
+
 class Data_Encoder_mol(data.Dataset):
     def __init__(self, train_file, tokenizer_config):
         'Initialization'
@@ -169,7 +171,6 @@ class Data_Encoder_mol(data.Dataset):
         bpe_codes_prot = codecs.open(tokenizer_config["vocab_pair_p"])
         self.pbpe = BPE(bpe_codes_prot, merges=-1, separator='')
 
-
     def __len__(self):
         'Denotes the total number of samples'
         return len(self.smile)
@@ -185,14 +186,13 @@ class Data_Encoder_mol(data.Dataset):
 
         input_seq = [self.begin_id] + d + [self.sep_id] + p + [self.sep_id]
         token_type_ids = np.concatenate((np.zeros((len(d) + 2), dtype=np.int), np.ones((len(p) + 1), dtype=np.int)))
-        token_type_ids = np.pad(token_type_ids, (0, self.max_len-len(input_seq)), 'constant', constant_values=0)
+        token_type_ids = np.pad(token_type_ids, (0, self.max_len - len(input_seq)), 'constant', constant_values=0)
         input, input_mask = seq2emb_encoder(input_seq, self.max_len, self.vocab)
         return torch.from_numpy(input).long(), torch.from_numpy(token_type_ids).long(), torch.from_numpy(input_mask).long(), y
         # return len(d), len(p)
 
 
 def get_task(task_name):
-
     tokenizer_config = {"vocab_file": './config/vocab.txt',
                         "vocab_pair": './config/drug_codes_chembl.txt',
                         "begin_id": '[CLS]',
@@ -255,12 +255,9 @@ def get_task(task_name):
                             "max_len": 595
                             }
 
-
         return df_train, tokenizer_config
 
 
-
-
 if __name__ == "__main__":
     # local test
     # dataFolder = './IC50/SPS/train_smile'

test.py

@@ -3,11 +3,15 @@ from modeling_bert import BertForMaskedLM
 import torch
 
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = BertForMaskedLM.from_pretrained('bert-base-uncased')
-inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")
+# model = BertForMaskedLM.from_pretrained('bert-base-uncased')
+seq_a = "The capital of France is [MASK]."
+seq_b = "The capital of France is Paris."
+choice0 = "It is eaten with a fork and a knife."
+choice1 = "It is eaten while held in the hand."
+
+
+inputs = tokenizer([[seq_a, seq_b], [choice0, choice1]], padding=True)
 labels = tokenizer("The capital of France is Paris.", return_tensors="pt") #["input_ids"]
-outputs = model(**inputs, labels=labels)
-loss = outputs.loss
-logits = outputs.logits
+
 
 print('----------------')
\ No newline at end of file