diff --git a/mmedit/datasets/pipelines/augmentation.py b/mmedit/datasets/pipelines/augmentation.py
index a36d427b8c..ea9faba214 100644
--- a/mmedit/datasets/pipelines/augmentation.py
+++ b/mmedit/datasets/pipelines/augmentation.py
@@ -1,7 +1,6 @@
 import math
 import numbers
 import os.path as osp
-import random
 
 import cv2
 import mmcv
@@ -611,7 +610,7 @@ def __init__(self, keys, binary_thr=0., kernel_min=9, kernel_max=49):
         self.binary_thr = binary_thr
 
     def _random_dilate(self, img):
-        kernel_size = random.randint(self.kernel_min, self.kernel_max)
+        kernel_size = np.random.randint(self.kernel_min, self.kernel_max + 1)
         kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
         dilate_kernel_size = kernel_size
         img_ = cv2.dilate(img, kernel, iterations=1)
diff --git a/mmedit/datasets/pipelines/crop.py b/mmedit/datasets/pipelines/crop.py
index c7f0520700..df4443f6f7 100644
--- a/mmedit/datasets/pipelines/crop.py
+++ b/mmedit/datasets/pipelines/crop.py
@@ -1,5 +1,3 @@
-import random
-
 import mmcv
 import numpy as np
 from torch.nn.modules.utils import _pair
@@ -214,8 +212,8 @@ def __call__(self, results):
                 f'{results["lq_path"][0]} and {results["gt_path"][0]}.')
 
         # randomly choose top and left coordinates for lq patch
-        top = random.randint(0, h_lq - lq_patch_size)
-        left = random.randint(0, w_lq - lq_patch_size)
+        top = np.random.randint(h_lq - lq_patch_size + 1)
+        left = np.random.randint(w_lq - lq_patch_size + 1)
         # crop lq patch
         results['lq'] = [
             v[top:top + lq_patch_size, left:left + lq_patch_size, ...]