diff --git a/.gitignore b/.gitignore
index 032d2dd..2dea1cc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@ logs/
 *__pycache__*
 *.sh
 datasets
-basicsr.egg-info
\ No newline at end of file
+basicsr.egg-info
+.eggs/
\ No newline at end of file
diff --git a/basicsr/models/image_restoration_model.py b/basicsr/models/image_restoration_model.py
index 1eec564..6cb39eb 100644
--- a/basicsr/models/image_restoration_model.py
+++ b/basicsr/models/image_restoration_model.py
@@ -375,27 +375,120 @@ class ImageRestorationModel(BaseModel):
             for key in metrics_dict:
                 metrics_dict[key] /= cnt
 
+            self.metric_results = metrics_dict
             self._log_validation_metric_values(current_iter, dataloader.dataset.opt['name'],
-                                               tb_logger, metrics_dict)
+                                               tb_logger)
         return 0.
 
-    def nondist_validation(self, *args, **kwargs):
-        logger = get_root_logger()
-        logger.warning('nondist_validation is not implemented. Run dist_validation.')
-        self.dist_validation(*args, **kwargs)
+    # def nondist_validation(self, *args, **kwargs):
+    #     logger = get_root_logger()
+    #     logger.warning('nondist_validation is not implemented. Run dist_validation.')
+    #     self.dist_validation(*args, **kwargs)
 
+    def nondist_validation(self, dataloader, current_iter, tb_logger,
+                           save_img, rgb2bgr, use_image):
+        dataset_name = dataloader.dataset.opt['name']
+        with_metrics = self.opt['val'].get('metrics') is not None
+        if with_metrics:
+            self.metric_results = {
+                metric: 0
+                for metric in self.opt['val']['metrics'].keys()
+            }
+        pbar = tqdm(total=len(dataloader), unit='image')
+
+        cnt = 0
+
+        for idx, val_data in enumerate(dataloader):
+            img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
+            # if img_name[-1] != '9':
+            #     continue
+
+            # print('val_data .. ', val_data['lq'].size(), val_data['gt'].size())
+            self.feed_data(val_data)
+            if self.opt['val'].get('grids', False):
+                self.grids()
+
+            self.test()
+
+            if self.opt['val'].get('grids', False):
+                self.grids_inverse()
+
+            visuals = self.get_current_visuals()
+            sr_img = tensor2img([visuals['result']], rgb2bgr=rgb2bgr)
+            if 'gt' in visuals:
+                gt_img = tensor2img([visuals['gt']], rgb2bgr=rgb2bgr)
+                del self.gt
+
+            # tentative for out of GPU memory
+            del self.lq
+            del self.output
+            torch.cuda.empty_cache()
+
+            if save_img:
+                
+                if self.opt['is_train']:
+                    
+                    save_img_path = osp.join(self.opt['path']['visualization'],
+                                             img_name,
+                                             f'{img_name}_{current_iter}.png')
+                    
+                    save_gt_img_path = osp.join(self.opt['path']['visualization'],
+                                             img_name,
+                                             f'{img_name}_{current_iter}_gt.png')
+                else:
+                    
+                    save_img_path = osp.join(
+                        self.opt['path']['visualization'], dataset_name,
+                        f'{img_name}.png')
+                    save_gt_img_path = osp.join(
+                        self.opt['path']['visualization'], dataset_name,
+                        f'{img_name}_gt.png')
+                    
+                imwrite(sr_img, save_img_path)
+                imwrite(gt_img, save_gt_img_path)
+
+            if with_metrics:
+                # calculate metrics
+                opt_metric = deepcopy(self.opt['val']['metrics'])
+                if use_image:
+                    for name, opt_ in opt_metric.items():
+                        metric_type = opt_.pop('type')
+                        self.metric_results[name] += getattr(
+                            metric_module, metric_type)(sr_img, gt_img, **opt_)
+                else:
+                    for name, opt_ in opt_metric.items():
+                        metric_type = opt_.pop('type')
+                        self.metric_results[name] += getattr(
+                            metric_module, metric_type)(visuals['result'], visuals['gt'], **opt_)
+
+            pbar.update(1)
+            pbar.set_description(f'Test {img_name}')
+            cnt += 1
+            # if cnt == 300:
+            #     break
+        pbar.close()
+
+        current_metric = 0.
+        if with_metrics:
+            for metric in self.metric_results.keys():
+                self.metric_results[metric] /= cnt
+                current_metric = self.metric_results[metric]
+
+            self._log_validation_metric_values(current_iter, dataset_name,
+                                               tb_logger)
+        return current_metric
 
     def _log_validation_metric_values(self, current_iter, dataset_name,
-                                      tb_logger, metric_dict):
+                                      tb_logger):
         log_str = f'Validation {dataset_name}, \t'
-        for metric, value in metric_dict.items():
+        for metric, value in self.metric_results.items():
             log_str += f'\t # {metric}: {value:.4f}'
         logger = get_root_logger()
         logger.info(log_str)
 
         log_dict = OrderedDict()
         # for name, value in loss_dict.items():
-        for metric, value in metric_dict.items():
+        for metric, value in self.metric_results.items():
             log_dict[f'm_{metric}'] = value
 
         self.log_dict = log_dict
diff --git a/basicsr/train.py b/basicsr/train.py
index 9cc8f2a..9c01bb2 100644
--- a/basicsr/train.py
+++ b/basicsr/train.py
@@ -155,7 +155,7 @@ def main():
     import os
     try:
         states = os.listdir(state_folder_path)
-    except:
+    except Exception:
         states = []
 
     resume_state = None
diff --git a/basicsr/version.py b/basicsr/version.py
index 2c4fdd3..773eff1 100644
--- a/basicsr/version.py
+++ b/basicsr/version.py
@@ -1,5 +1,5 @@
 # GENERATED VERSION FILE
-# TIME: Mon Apr 18 21:35:20 2022
-__version__ = '1.2.0+386ca20'
+# TIME: Fri May  5 22:09:31 2023
+__version__ = '1.2.0+50cb149'
 short_version = '1.2.0'
 version_info = (1, 2, 0)
diff --git a/options/test/dashboard/NAFNet-width32.yml b/options/test/dashboard/NAFNet-width32.yml
new file mode 100644
index 0000000..e68eefe
--- /dev/null
+++ b/options/test/dashboard/NAFNet-width32.yml
@@ -0,0 +1,60 @@
+# ------------------------------------------------------------------------
+# Copyright (c) 2022 megvii-model. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from BasicSR (https://github.com/xinntao/BasicSR)
+# Copyright 2018-2020 BasicSR Authors
+# ------------------------------------------------------------------------
+# general settings
+name: NAFNet-Dashboard-width32-test
+model_type: ImageRestorationModel
+scale: 1
+num_gpu: 1 # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+
+  test:
+    name: dashboard-test
+    type: PairedImageDataset
+
+    dataroot_gt: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\gt
+    dataroot_lq: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\lq
+
+    io_backend:
+      type: disk
+
+# network structures
+network_g:
+  type: NAFNetLocal
+  width: 32
+  enc_blk_nums: [1, 1, 1, 14]
+  middle_blk_num: 1
+  dec_blk_nums: [1, 1, 1, 1]
+
+# path
+path:
+  pretrain_network_g: experiments/pretrained_models/NAFNet-dashboard-width32.pth
+  strict_load_g: true
+  resume_state: ~
+
+# validation settings
+val:
+  save_img: true
+  grids: false
+
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+    ssim:
+      type: calculate_ssim
+      crop_border: 0
+      test_y_channel: false
+
+# dist training settings
+# dist_params:
+#   backend: nccl
+#   port: 29500
diff --git a/options/train/dashboard/NAFNet-width32.yml b/options/train/dashboard/NAFNet-width32.yml
new file mode 100644
index 0000000..80f921f
--- /dev/null
+++ b/options/train/dashboard/NAFNet-width32.yml
@@ -0,0 +1,108 @@
+# ------------------------------------------------------------------------
+# Copyright (c) 2022 megvii-model. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from BasicSR (https://github.com/xinntao/BasicSR)
+# Copyright 2018-2020 BasicSR Authors
+# ------------------------------------------------------------------------
+# general settings
+name: NAFNet-Dashboard-width32
+model_type: ImageRestorationModel
+scale: 1
+num_gpu: 1
+manual_seed: 10
+
+datasets:
+  train:
+    name: dashboard-train
+    type: PairedImageDataset
+    dataroot_gt: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\train\gt
+    dataroot_lq: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\train\lq
+
+    filename_tmpl: '{}'
+    io_backend:
+      type: disk
+
+    gt_size: 128    # Cropped patched size for gt patches.
+    use_flip: true
+    use_rot: true
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 20
+    batch_size_per_gpu: 80
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  val:
+    name: dashboard-val
+    type: PairedImageDataset
+    dataroot_gt: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\gt
+    dataroot_lq: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\lq
+    io_backend:
+      type: disk
+
+
+network_g:
+  type: NAFNetLocal   # network name, see in models/archs/NAFNet_arch.py
+  width: 32
+  enc_blk_nums: [1, 1, 1, 14]
+  middle_blk_num: 1
+  dec_blk_nums: [1, 1, 1, 1]
+
+# path
+path:
+  pretrain_network_g: ~
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  optim_g:
+    type: AdamW
+    lr: !!float 1e-3
+    weight_decay: !!float 1e-3
+    betas: [0.9, 0.9]
+
+  scheduler:
+    type: TrueCosineAnnealingLR
+    T_max: 1000
+    eta_min: !!float 1e-7
+
+  total_iter: 1000
+  warmup_iter: -1 # no warm up
+
+  # losses
+  pixel_opt:
+    type: PSNRLoss
+    loss_weight: 1
+    reduction: mean
+
+# validation settings
+val:
+  val_freq: !!float 2e2
+  save_img: false
+
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+    ssim:
+      type: calculate_ssim
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 10
+  save_checkpoint_freq: !!float 2e2
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+# dist_params:
+#   backend: nccl
+#   port: 29500