Update dataset.py

dataset.py

@@ -3,7 +3,6 @@ import os
 import os.path as osp
 import json
 import numpy as np
-# from konlpy.tag import Okt  
 
 import torch
 import torch.nn.functional as F
@@ -240,11 +239,6 @@ class JointDataset(Dataset):
                 # debug
                 for ent_k, ent_h in zip(ent_pos_kor, ent_pos_han):
                     assert len(ent_k) ==  len(ent_h)
-                #         print(json_file)
-                #         pprint.pprint(ex["entity"])
-                #         print(entities_kor)
-                #         print(entities_han)
-                #         break
 
                 
                 ### labels ###
@@ -259,19 +253,7 @@ class JointDataset(Dataset):
                     if h_idx is None or t_idx is None:
                         num_filtered_labels += 1
                         continue
-                        
-                    # TODO: idx has to match across languages, otherwise the label won't be universal.
-                    # if h_idx != h_idx2 or t_idx != t_idx2:
-                    #     import pdb; pdb.set_trace()
-                    # assert h_idx == h_idx2 and t_idx == t_idx2
                     
-                    # debugging
-                    if not( h_idx == h_idx2 and t_idx == t_idx2) :
-                        # print(f"fname: {json_file}")
-                        # pprint.pprint(relation)
-                        N_data_problems += 1
-                        continue
-
                     r_idx = self.label_map[relation["kor"]["label"]]
                     labels[h_idx, t_idx, r_idx] = 1
                 
@@ -292,16 +274,6 @@ class JointDataset(Dataset):
                     "text_han": ex["text"]["han"] 
                 })
 
-                #     self.features.append({
-                #         "input_ids_kor": input_ids_kor,
-                #         "input_ids_han": input_ids_han,
-                #         "ent_pos_kor": ent_pos_kor,
-                #         "ent_pos_han": ent_pos_han,
-                #         "labels": labels
-                #     })
-
-        print(f"# problems in (h_idx == h_idx2 and t_idx == t_idx2) : {N_data_problems}")
-
         logging.info(f"# of empty entity examples filtered: {num_empty_entity_examples}")
         logging.info(f"# of empty label examples filtered: {num_empty_label_examples}")
         logging.info(f"# of beyond-truncated-text labels filtered: {num_filtered_labels}")
@@ -362,8 +334,6 @@ class KoreanDataset(Dataset):
         self.split = split
         self.features = []
 
-        # self.word_tokenizer = Okt()
-
         self.save_dir = osp.join(args.data_dir, args.language)
         self.save_path = osp.join(self.save_dir, f"{args.model_type}_{split}.pt")
         os.makedirs(self.save_dir, exist_ok=True)        
@@ -392,7 +362,6 @@ class KoreanDataset(Dataset):
         
         logging.info(f"Creating features from {self.args.data_dir}")
         rootdir = osp.join(self.args.data_dir, f"{self.split}")
-        # print(f"Current directory: {rootdir}")
 
         for json_file in tqdm(os.listdir(rootdir), desc="Converting examples to features"):
             with open(osp.join(rootdir, json_file), encoding='utf-8') as f:
@@ -478,28 +447,6 @@ class KoreanDataset(Dataset):
                     ent_pos[-1].append((token_start, token_end))
                     # ent_ner[-1].append(ment[-1])
 
-            # ent_masks, ent_ners = [], []
-            # for ent in entities:
-            #     ent_mask = np.zeros(len(input_ids), dtype=np.float32)
-            #     ent_ner = np.zeros(len(input_ids), dtype=np.float32)
-
-            #     for ment in ent:
-            #         start, end = ment[3], ment[4]
-            #         # Skip entity mentions that appear beyond the truncated text
-            #         if (start > self.args.max_seq_length-num_special_tokens or
-            #             end > self.args.max_seq_length-num_special_tokens):
-            #             continue
-            #         ent_mask[start:end] = 1
-            #         ent_ner[start:end] = self.ner_map[ment[5]]
-
-            #     assert ent_mask.sum() != 0
-            
-            #     ent_masks.append(ent_mask)
-            #     ent_ners.append(ent_ner)
-
-            # ent_masks = np.stack(ent_masks, axis=0)
-            # ent_ners = np.stack(ent_ners, axis=0)
-            
             ### labels ###
             labels = torch.zeros((len(entities), len(entities), self.config.num_labels), dtype=torch.float32)
             for relation in ex["relation"]:
@@ -517,28 +464,13 @@ class KoreanDataset(Dataset):
                 for t in range(len(entities)):
                     if torch.all(labels[h][t] == 0):
                         labels[h][t][0] = 1
-
-            ### label mask ###
-            # label_mask = np.ones((len(entities), len(entities)), dtype='bool')
-            # np.fill_diagonal(label_mask, 0) # ignore diagonals
-
-            # TODO: normalize ent_masks (test normalization vs. not)
-            # ent_masks = ent_masks / np.expand_dims(ent_masks.sum(1), axis=1)
-
+                        
             self.features.append({
                 "input_ids": input_ids,
                 "ent_pos": ent_pos,
                 "labels": labels,
             })
 
-            # self.features.append({
-            #     "input_ids": input_ids,
-            #     "ent_masks": ent_masks,
-            #     "ent_ners": ent_ners,
-            #     "labels": labels,
-            #     "label_mask": label_mask
-            # })
-
         logging.info(f"# of empty entity examples filtered: {num_empty_entity_examples}")
         logging.info(f"# of empty label examples filtered: {num_empty_label_examples}")
         logging.info(f"# of beyond-truncated-text labels filtered: {num_filtered_labels}")
@@ -548,32 +480,15 @@ class KoreanDataset(Dataset):
 
     def collate_fn(self, samples):
         input_ids = [x["input_ids"] for x in samples]
-
         ent_pos = [x["ent_pos"] for x in samples]
-        # max_ent_len = max([len(x["ent_pos"]) for x in samples])
-        # ent_masks = [F.pad(torch.from_numpy(x["ent_masks"]), \
-        #             (0, 0, 0, max_ent_len-x["ent_masks"].shape[0])) for x in samples]
-        # ent_ners = [F.pad(torch.from_numpy(x["ent_ners"]), \
-        #             (0, 0, 0, max_ent_len-x["ent_ners"].shape[0])) for x in samples]
-
         labels = [x["labels"].view(-1, self.config.num_labels) for x in samples]
-        # labels = [F.pad(torch.from_numpy(x["labels"]), \
-        #             (0, 0, 0, max_ent_len-x["labels"].shape[0], 0, max_ent_len-x["labels"].shape[1]), value=-100) for x in samples]
-        # label_mask = [F.pad(torch.from_numpy(x["label_mask"]), \
-        #             (0, max_ent_len-x["label_mask"].shape[0], 0, max_ent_len-x["label_mask"].shape[1])) for x in samples]
 
         input_ids = torch.tensor(input_ids, dtype=torch.long)
-        # ent_masks = torch.stack(ent_masks, dim=0)
         labels = torch.cat(labels, dim=0)
-        # labels = torch.stack(labels, dim=0)
-        # label_mask = torch.stack(label_mask, dim=0)
         
         return {"input_ids": input_ids,
                 "ent_pos": ent_pos,
-                # "ent_masks": ent_masks,
-                # "ent_ners": ent_ners,
                 "labels": labels,
-                # "label_mask": label_mask,
                 }
 
     def __len__(self):
@@ -623,7 +538,6 @@ class HanjaDataset(Dataset):
         
         logging.info(f"Creating features from {self.args.data_dir}")
         rootdir = osp.join(self.args.data_dir, f"{self.split}")
-        # print(f"Current directory: {rootdir}")
 
         for json_file in tqdm(os.listdir(rootdir), desc="Converting examples to features"):
             with open(osp.join(rootdir, json_file), encoding='utf-8') as f:
@@ -702,34 +616,10 @@ class HanjaDataset(Dataset):
             ent_pos, ent_ner = [], []
             for ent in entities:
                 ent_pos.append([])
-                # ent_ner.append([])
                 for ment in ent:
                     token_start, token_end = ment[3], ment[4]
                     ent_pos[-1].append((token_start, token_end))
-                    # ent_ner[-1].append(ment[-1])
-
-            # ent_masks, ent_ners = [], []
-            # for ent in entities:
-            #     ent_mask = np.zeros(len(input_ids), dtype=np.float32)
-            #     ent_ner = np.zeros(len(input_ids), dtype=np.float32)
-
-            #     for ment in ent:
-            #         start, end = ment[3], ment[4]
-            #         # Skip entity mentions that appear beyond the truncated text
-            #         if (start > self.args.max_seq_length-num_special_tokens or
-            #             end > self.args.max_seq_length-num_special_tokens):
-            #             continue
-            #         ent_mask[start:end] = 1
-            #         ent_ner[start:end] = self.ner_map[ment[5]]
-
-            #     assert ent_mask.sum() != 0
-            
-            #     ent_masks.append(ent_mask)
-            #     ent_ners.append(ent_ner)
 
-            # ent_masks = np.stack(ent_masks, axis=0)
-            # ent_ners = np.stack(ent_ners, axis=0)
-            
             ### labels ###
             labels = torch.zeros((len(entities), len(entities), self.config.num_labels), dtype=torch.float32)
             for relation in ex["relation"]:
@@ -748,27 +638,12 @@ class HanjaDataset(Dataset):
                     if torch.all(labels[h][t] == 0):
                         labels[h][t][0] = 1
 
-            ### label mask ###
-            # label_mask = np.ones((len(entities), len(entities)), dtype='bool')
-            # np.fill_diagonal(label_mask, 0) # ignore diagonals
-
-            # TODO: normalize ent_masks (test normalization vs. not)
-            # ent_masks = ent_masks / np.expand_dims(ent_masks.sum(1), axis=1)
-
             self.features.append({
                 "input_ids": input_ids,
                 "ent_pos": ent_pos,
                 "labels": labels,
             })
 
-            # self.features.append({
-            #     "input_ids": input_ids,
-            #     "ent_masks": ent_masks,
-            #     "ent_ners": ent_ners,
-            #     "labels": labels,
-            #     "label_mask": label_mask
-            # })
-
         logging.info(f"# of empty entity examples filtered: {num_empty_entity_examples}")
         logging.info(f"# of empty label examples filtered: {num_empty_label_examples}")
         logging.info(f"# of beyond-truncated-text labels filtered: {num_filtered_labels}")
@@ -779,30 +654,13 @@ class HanjaDataset(Dataset):
         input_ids = [x["input_ids"] for x in samples]
 
         ent_pos = [x["ent_pos"] for x in samples]
-        # max_ent_len = max([len(x["ent_pos"]) for x in samples])
-        # ent_masks = [F.pad(torch.from_numpy(x["ent_masks"]), \
-        #             (0, 0, 0, max_ent_len-x["ent_masks"].shape[0])) for x in samples]
-        # ent_ners = [F.pad(torch.from_numpy(x["ent_ners"]), \
-        #             (0, 0, 0, max_ent_len-x["ent_ners"].shape[0])) for x in samples]
-
         labels = [x["labels"].view(-1, self.config.num_labels) for x in samples]
-        # labels = [F.pad(torch.from_numpy(x["labels"]), \
-        #             (0, 0, 0, max_ent_len-x["labels"].shape[0], 0, max_ent_len-x["labels"].shape[1]), value=-100) for x in samples]
-        # label_mask = [F.pad(torch.from_numpy(x["label_mask"]), \
-        #             (0, max_ent_len-x["label_mask"].shape[0], 0, max_ent_len-x["label_mask"].shape[1])) for x in samples]
-
         input_ids = torch.tensor(input_ids, dtype=torch.long)
-        # ent_masks = torch.stack(ent_masks, dim=0)
         labels = torch.cat(labels, dim=0)
-        # labels = torch.stack(labels, dim=0)
-        # label_mask = torch.stack(label_mask, dim=0)
         
         return {"input_ids": input_ids,
                 "ent_pos": ent_pos,
-                # "ent_masks": ent_masks,
-                # "ent_ners": ent_ners,
                 "labels": labels,
-                # "label_mask": label_mask,
                 }
 
     def __len__(self):