diff --git a/tutorials/train/image_restoration/drn_train.py b/tutorials/train/image_restoration/drn_train.py
index 899338c..ecfc358 100644
--- a/tutorials/train/image_restoration/drn_train.py
+++ b/tutorials/train/image_restoration/drn_train.py
@@ -5,77 +5,76 @@ sys.path.append(os.path.abspath('../PaddleRS'))
 import paddle
 import paddlers as pdrs
 
-if __name__ == "__main__":
+# 定义训练和验证时的transforms
+train_transforms = pdrs.datasets.ComposeTrans(
+    input_keys=['lq', 'gt'],
+    output_keys=['lq', 'lqx2', 'gt'],
+    pipelines=[{
+        'name': 'SRPairedRandomCrop',
+        'gt_patch_size': 192,
+        'scale': 4,
+        'scale_list': True
+    }, {
+        'name': 'PairedRandomHorizontalFlip'
+    }, {
+        'name': 'PairedRandomVerticalFlip'
+    }, {
+        'name': 'PairedRandomTransposeHW'
+    }, {
+        'name': 'Transpose'
+    }, {
+        'name': 'Normalize',
+        'mean': [0.0, 0.0, 0.0],
+        'std': [1.0, 1.0, 1.0]
+    }])
 
-    # 定义训练和验证时的transforms
-    train_transforms = pdrs.datasets.ComposeTrans(
-        input_keys=['lq', 'gt'],
-        output_keys=['lq', 'lqx2', 'gt'],
-        pipelines=[{
-            'name': 'SRPairedRandomCrop',
-            'gt_patch_size': 192,
-            'scale': 4,
-            'scale_list': True
-        }, {
-            'name': 'PairedRandomHorizontalFlip'
-        }, {
-            'name': 'PairedRandomVerticalFlip'
-        }, {
-            'name': 'PairedRandomTransposeHW'
-        }, {
-            'name': 'Transpose'
-        }, {
-            'name': 'Normalize',
-            'mean': [0.0, 0.0, 0.0],
-            'std': [1.0, 1.0, 1.0]
-        }])
+test_transforms = pdrs.datasets.ComposeTrans(
+    input_keys=['lq', 'gt'],
+    output_keys=['lq', 'gt'],
+    pipelines=[{
+        'name': 'Transpose'
+    }, {
+        'name': 'Normalize',
+        'mean': [0.0, 0.0, 0.0],
+        'std': [1.0, 1.0, 1.0]
+    }])
 
-    test_transforms = pdrs.datasets.ComposeTrans(
-        input_keys=['lq', 'gt'],
-        output_keys=['lq', 'gt'],
-        pipelines=[{
-            'name': 'Transpose'
-        }, {
-            'name': 'Normalize',
-            'mean': [0.0, 0.0, 0.0],
-            'std': [1.0, 1.0, 1.0]
-        }])
+# 定义训练集
+train_gt_floder = r"../work/RSdata_for_SR/trian_HR"  # 高分辨率影像所在路径
+train_lq_floder = r"../work/RSdata_for_SR/train_LR/x4"  # 低分辨率影像所在路径
+num_workers = 4
+batch_size = 8
+scale = 4
+train_dataset = pdrs.datasets.SRdataset(
+    mode='train',
+    gt_floder=train_gt_floder,
+    lq_floder=train_lq_floder,
+    transforms=train_transforms(),
+    scale=scale,
+    num_workers=num_workers,
+    batch_size=batch_size)
+train_dict = train_dataset()
 
-    # 定义训练集
-    train_gt_floder = r"../work/RSdata_for_SR/trian_HR"  # 高分辨率影像所在路径
-    train_lq_floder = r"../work/RSdata_for_SR/train_LR/x4"  # 低分辨率影像所在路径
-    num_workers = 4
-    batch_size = 8
-    scale = 4
-    train_dataset = pdrs.datasets.SRdataset(
-        mode='train',
-        gt_floder=train_gt_floder,
-        lq_floder=train_lq_floder,
-        transforms=train_transforms(),
-        scale=scale,
-        num_workers=num_workers,
-        batch_size=batch_size)
-    train_dict = train_dataset()
+# 定义测试集
+test_gt_floder = r"../work/RSdata_for_SR/test_HR"
+test_lq_floder = r"../work/RSdata_for_SR/test_LR/x4"
+test_dataset = pdrs.datasets.SRdataset(
+    mode='test',
+    gt_floder=test_gt_floder,
+    lq_floder=test_lq_floder,
+    transforms=test_transforms(),
+    scale=scale)
 
-    # 定义测试集
-    test_gt_floder = r"../work/RSdata_for_SR/test_HR"
-    test_lq_floder = r"../work/RSdata_for_SR/test_LR/x4"
-    test_dataset = pdrs.datasets.SRdataset(
-        mode='test',
-        gt_floder=test_gt_floder,
-        lq_floder=test_lq_floder,
-        transforms=test_transforms(),
-        scale=scale)
+# 初始化模型，可以对网络结构的参数进行调整
+model = pdrs.tasks.DRNet(
+    n_blocks=30, n_feats=16, n_colors=3, rgb_range=255, negval=0.2)
 
-    # 初始化模型，可以对网络结构的参数进行调整
-    model = pdrs.tasks.DRNet(
-        n_blocks=30, n_feats=16, n_colors=3, rgb_range=255, negval=0.2)
-
-    model.train(
-        total_iters=100000,
-        train_dataset=train_dataset(),
-        test_dataset=test_dataset(),
-        output_dir='output_dir',
-        validate=5000,
-        snapshot=5000,
-        lr_rate=0.0001)
+model.train(
+    total_iters=100000,
+    train_dataset=train_dataset(),
+    test_dataset=test_dataset(),
+    output_dir='output_dir',
+    validate=5000,
+    snapshot=5000,
+    lr_rate=0.0001,
+    log=10)