diff --git a/data/realsr_preprocess/imresize.py b/data/realsr_preprocess/imresize.py
index 02fa92a19a7a4013381f7baf7ab1470100876e8c..6d61bc7caa2618d8617f3c5c9128f577c29cec53 100644
--- a/data/realsr_preprocess/imresize.py
+++ b/data/realsr_preprocess/imresize.py
@@ -218,7 +218,7 @@ def kernel_shift(kernel, sf):
 
     # Before applying the shift, we first pad the kernel so that nothing is lost due to the shift
     # (biggest shift among dims + 1 for safety)
-    kernel = np.pad(kernel, np.int(np.ceil(np.max(shift_vec))) + 1, 'constant')
+    kernel = np.pad(kernel, np.int_(np.ceil(np.max(shift_vec))) + 1, 'constant')
 
     # Finally shift the kernel and return
     return interpolation.shift(kernel, shift_vec)
diff --git a/ppgan/faceutils/face_detection/utils.py b/ppgan/faceutils/face_detection/utils.py
index 6590f9662252d26dd67c4adc2e82b3b86a8123a1..570b2c259222ffde1876b3f459b29bed8f3653e0 100644
--- a/ppgan/faceutils/face_detection/utils.py
+++ b/ppgan/faceutils/face_detection/utils.py
@@ -59,7 +59,7 @@ def crop(image, center, scale, resolution=256.0):
                           dtype=np.int32)
         newImg = np.zeros(newDim, dtype=np.uint8)
     else:
-        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int_)
         newImg = np.zeros(newDim, dtype=np.uint8)
     ht = image.shape[0]
     wd = image.shape[1]
diff --git a/ppgan/models/generators/generator_starganv2.py b/ppgan/models/generators/generator_starganv2.py
index e5bbce0f980df64699b426503b9940edf35ede15..636df792e23970229453ab161ce98261bc398ae1 100755
--- a/ppgan/models/generators/generator_starganv2.py
+++ b/ppgan/models/generators/generator_starganv2.py
@@ -240,8 +240,8 @@ class StarGANv2Mapping(nn.Layer):
         s = []
         for i in range(idx.shape[0]):
             s += [
-                out[idx[i].numpy().astype(np.int).tolist()[0],
-                    y[i].numpy().astype(np.int).tolist()[0]]
+                out[idx[i].numpy().astype(np.int_).tolist()[0],
+                    y[i].numpy().astype(np.int_).tolist()[0]]
             ]
         s = paddle.stack(s)
         s = paddle.reshape(s, (s.shape[0], -1))
@@ -286,8 +286,8 @@ class StarGANv2Style(nn.Layer):
         s = []
         for i in range(idx.shape[0]):
             s += [
-                out[idx[i].numpy().astype(np.int).tolist()[0],
-                    y[i].numpy().astype(np.int).tolist()[0]]
+                out[idx[i].numpy().astype(np.int_).tolist()[0],
+                    y[i].numpy().astype(np.int_).tolist()[0]]
             ]
         s = paddle.stack(s)
         s = paddle.reshape(s, (s.shape[0], -1))
diff --git a/requirements.txt b/requirements.txt
index 8b072c66e7e2d8c2957a823680e55b7838bebbf2..6eeab8951a910d8d10c573f55d0619db78394519 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,11 +2,11 @@ tqdm
 PyYAML>=5.1
 scikit-image>=0.14.0
 scipy>=1.1.0
-opencv-python <= 4.6.0
+opencv-python==4.6.0.66
 imageio==2.9.0
 imageio-ffmpeg
 librosa==0.8.1
-numba==0.53.1
+numba
 easydict
 munch
 natsort