--- a
+++ b/opengait/data/dataset.py
@@ -0,0 +1,125 @@
+import os
+import pickle
+import os.path as osp
+import torch.utils.data as tordata
+import json
+from utils import get_msg_mgr
+
+
+class DataSet(tordata.Dataset):
+    def __init__(self, data_cfg, training):
+        """
+            seqs_info: the list with each element indicating 
+                            a certain gait sequence presented as [label, type, view, paths];
+        """
+        self.__dataset_parser(data_cfg, training)
+        self.cache = data_cfg['cache']
+        self.label_list = [seq_info[0] for seq_info in self.seqs_info]
+        self.types_list = [seq_info[1] for seq_info in self.seqs_info]
+        self.views_list = [seq_info[2] for seq_info in self.seqs_info]
+
+        self.label_set = sorted(list(set(self.label_list)))
+        self.types_set = sorted(list(set(self.types_list)))
+        self.views_set = sorted(list(set(self.views_list)))
+        self.seqs_data = [None] * len(self)
+        self.indices_dict = {label: [] for label in self.label_set}
+        for i, seq_info in enumerate(self.seqs_info):
+            self.indices_dict[seq_info[0]].append(i)
+        if self.cache:
+            self.__load_all_data()
+
+    def __len__(self):
+        return len(self.seqs_info)
+
+    def __loader__(self, paths):
+        paths = sorted(paths)
+        data_list = []
+        for pth in paths:
+            if pth.endswith('.pkl'):
+                with open(pth, 'rb') as f:
+                    _ = pickle.load(f)
+                f.close()
+            else:
+                raise ValueError('- Loader - just support .pkl !!!')
+            data_list.append(_)
+        for idx, data in enumerate(data_list):
+            if len(data) != len(data_list[0]):
+                raise ValueError(
+                    'Each input data({}) should have the same length.'.format(paths[idx]))
+            if len(data) == 0:
+                raise ValueError(
+                    'Each input data({}) should have at least one element.'.format(paths[idx]))
+        return data_list
+
+    def __getitem__(self, idx):
+        if not self.cache:
+            data_list = self.__loader__(self.seqs_info[idx][-1])
+        elif self.seqs_data[idx] is None:
+            data_list = self.__loader__(self.seqs_info[idx][-1])
+            self.seqs_data[idx] = data_list
+        else:
+            data_list = self.seqs_data[idx]
+        seq_info = self.seqs_info[idx]
+        return data_list, seq_info
+
+    def __load_all_data(self):
+        for idx in range(len(self)):
+            self.__getitem__(idx)
+
+    def __dataset_parser(self, data_config, training):
+        dataset_root = data_config['dataset_root']
+        try:
+            data_in_use = data_config['data_in_use']  # [n], true or false
+        except:
+            data_in_use = None
+
+        with open(data_config['dataset_partition'], "rb") as f:
+            partition = json.load(f)
+        train_set = partition["TRAIN_SET"]
+        test_set = partition["TEST_SET"]
+        label_list = os.listdir(dataset_root)
+        train_set = [label for label in train_set if label in label_list]
+        test_set = [label for label in test_set if label in label_list]
+        miss_pids = [label for label in label_list if label not in (
+            train_set + test_set)]
+        msg_mgr = get_msg_mgr()
+
+        def log_pid_list(pid_list):
+            if len(pid_list) >= 3:
+                msg_mgr.log_info('[%s, %s, ..., %s]' %
+                                 (pid_list[0], pid_list[1], pid_list[-1]))
+            else:
+                msg_mgr.log_info(pid_list)
+
+        if len(miss_pids) > 0:
+            msg_mgr.log_debug('-------- Miss Pid List --------')
+            msg_mgr.log_debug(miss_pids)
+        if training:
+            msg_mgr.log_info("-------- Train Pid List --------")
+            log_pid_list(train_set)
+        else:
+            msg_mgr.log_info("-------- Test Pid List --------")
+            log_pid_list(test_set)
+
+        def get_seqs_info_list(label_set):
+            seqs_info_list = []
+            for lab in label_set:
+                for typ in sorted(os.listdir(osp.join(dataset_root, lab))):
+                    for vie in sorted(os.listdir(osp.join(dataset_root, lab, typ))):
+                        seq_info = [lab, typ, vie]
+                        seq_path = osp.join(dataset_root, *seq_info)
+                        seq_dirs = sorted(os.listdir(seq_path))
+                        if seq_dirs != []:
+                            seq_dirs = [osp.join(seq_path, dir)
+                                        for dir in seq_dirs]
+                            if data_in_use is not None:
+                                seq_dirs = [dir for dir, use_bl in zip(
+                                    seq_dirs, data_in_use) if use_bl]
+                            seqs_info_list.append([*seq_info, seq_dirs])
+                        else:
+                            msg_mgr.log_debug(
+                                'Find no .pkl file in %s-%s-%s.' % (lab, typ, vie))
+            return seqs_info_list
+
+        self.seqs_info = get_seqs_info_list(
+            train_set) if training else get_seqs_info_list(test_set)