diff --git a/codes/data/__init__.py b/codes/data/__init__.py
index ce10a834..01bddc01 100644
--- a/codes/data/__init__.py
+++ b/codes/data/__init__.py
@@ -21,7 +21,7 @@ def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
                                            num_workers=num_workers, sampler=sampler, drop_last=True,
                                            pin_memory=False)
     else:
-        return torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1,
+        return torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0,
                                            pin_memory=False)
 
 
diff --git a/codes/data_scripts/extract_subimages.py b/codes/data_scripts/extract_subimages.py
index a8185df2..2849afe2 100644
--- a/codes/data_scripts/extract_subimages.py
+++ b/codes/data_scripts/extract_subimages.py
@@ -19,17 +19,17 @@ def main():
     # CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
     # compression time. If read raw images during training, use 0 for faster IO speed.
     if mode == 'single':
-        opt['input_folder'] = '../../datasets/DIV2K/DIV2K_train_HR'
-        opt['save_folder'] = '../../datasets/DIV2K/DIV2K800_sub'
+        opt['input_folder'] = '../../datasets/div2k/DIV2K_train_HR'
+        opt['save_folder'] = '../../datasets/div2k/DIV2K800_sub'
         opt['crop_sz'] = 480  # the size of each sub-image
         opt['step'] = 240  # step of the sliding crop window
         opt['thres_sz'] = 48  # size threshold
         extract_signle(opt)
     elif mode == 'pair':
-        GT_folder = '../../datasets/DIV2K/DIV2K_train_HR'
-        LR_folder = '../../datasets/DIV2K/DIV2K_train_LR_bicubic/X4'
-        save_GT_folder = '../../datasets/DIV2K/DIV2K800_sub'
-        save_LR_folder = '../../datasets/DIV2K/DIV2K800_sub_bicLRx4'
+        GT_folder = '../../datasets/div2k/DIV2K_train_HR'
+        LR_folder = '../../datasets/div2k/DIV2K_train_LR_bicubic/X4'
+        save_GT_folder = '../../datasets/div2k/DIV2K800_sub'
+        save_LR_folder = '../../datasets/div2k/DIV2K800_sub_bicLRx4'
         scale_ratio = 4
         crop_sz = 480  # the size of each sub-image (GT)
         step = 240  # step of the sliding crop window (GT)
diff --git a/codes/data_scripts/rename.py b/codes/data_scripts/rename.py
index f8a19552..ded86ed4 100644
--- a/codes/data_scripts/rename.py
+++ b/codes/data_scripts/rename.py
@@ -3,7 +3,7 @@ import glob
 
 
 def main():
-    folder = '../../datasets/DIV2K/DIV2K_train_LR_bicubic/X4'
+    folder = 'datasets/div2k/DIV2K_valid_LR_bicubic/X4'
     DIV2K(folder)
     print('Finished.')
 
diff --git a/codes/test.py b/codes/test.py
index b07a44b7..39ed79e5 100644
--- a/codes/test.py
+++ b/codes/test.py
@@ -12,7 +12,7 @@ from models import create_model
 
 #### options
 parser = argparse.ArgumentParser()
-parser.add_argument('-opt', type=str, required=True, help='Path to options YMAL file.')
+parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='options/test/test_ESRGAN_vrp.yml')
 opt = option.parse(parser.parse_args().opt, is_train=False)
 opt = option.dict_to_nonedict(opt)
 
diff --git a/codes/train.py b/codes/train.py
index c8c29bde..8721d33f 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -3,10 +3,11 @@ import math
 import argparse
 import random
 import logging
+from tqdm import tqdm
 
 import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
+#import torch.distributed as dist
+#import torch.multiprocessing as mp
 from data.data_sampler import DistIterSampler
 
 import options.options as option
@@ -28,7 +29,7 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='options/train/finetune_ESRGAN_blacked.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
@@ -138,7 +139,7 @@ def main():
         current_step = resume_state['iter']
         model.resume_training(resume_state)  # handle optimizers and schedulers
     else:
-        current_step = 0
+        current_step = -1
         start_epoch = 0
 
     #### training
@@ -146,7 +147,8 @@ def main():
     for epoch in range(start_epoch, total_epochs + 1):
         if opt['dist']:
             train_sampler.set_epoch(epoch)
-        for _, train_data in enumerate(train_loader):
+        tq_ldr = tqdm(train_loader)
+        for _, train_data in enumerate(tq_ldr):
             current_step += 1
             if current_step > total_iters:
                 break