diff --git a/third_part/GPEN/face_morpher/facemorpher/warper.py b/third_part/GPEN/face_morpher/facemorpher/warper.py
index 50b1a315..9f930a22 100644
--- a/third_part/GPEN/face_morpher/facemorpher/warper.py
+++ b/third_part/GPEN/face_morpher/facemorpher/warper.py
@@ -13,7 +13,7 @@ def bilinear_interpolate(img, coords):
   x0, y0 = int_coords
   dx, dy = coords - int_coords
 
-  # 4 Neighour pixels
+  # 4 Neighbour pixels
   q11 = img[y0, x0]
   q21 = img[y0, x0+1]
   q12 = img[y0+1, x0]
diff --git a/third_part/face3d/models/template_model.py b/third_part/face3d/models/template_model.py
index dac7b33d..6d511a6c 100644
--- a/third_part/face3d/models/template_model.py
+++ b/third_part/face3d/models/template_model.py
@@ -87,7 +87,7 @@ def forward(self):
 
     def backward(self):
         """Calculate losses, gradients, and update network weights; called in every training iteration"""
-        # caculate the intermediate results if necessary; here self.output has been computed during function <forward>
+        # calculate the intermediate results if necessary; here self.output has been computed during function <forward>
         # calculate loss given the input and intermediate results
         self.loss_G = self.criterionLoss(self.output, self.data_B) * self.opt.lambda_regression
         self.loss_G.backward()       # calculate gradients of network G w.r.t. loss_G
diff --git a/third_part/face3d/util/util.py b/third_part/face3d/util/util.py
index 0d689ca1..fda1509f 100644
--- a/third_part/face3d/util/util.py
+++ b/third_part/face3d/util/util.py
@@ -68,7 +68,7 @@ def tensor2im(input_image, imtype=np.uint8):
         image_numpy = image_tensor.clamp(0.0, 1.0).cpu().float().numpy()  # convert it into a numpy array
         if image_numpy.shape[0] == 1:  # grayscale to RGB
             image_numpy = np.tile(image_numpy, (3, 1, 1))
-        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0  # post-processing: tranpose and scaling
+        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0  # post-processing: transpose and scaling
     else:  # if it is a numpy array, do nothing
         image_numpy = input_image
     return image_numpy.astype(imtype)
diff --git a/utils/inference_utils.py b/utils/inference_utils.py
index 84025f17..9abf57eb 100644
--- a/utils/inference_utils.py
+++ b/utils/inference_utils.py
@@ -197,7 +197,7 @@ def Laplacian_Pyramid_Blending_with_mask(A, B, m, num_levels = 6):
     lpB  = [gpB[num_levels-1]]
     gpMr = [gpM[num_levels-1]]
     for i in range(num_levels-1,0,-1):
-        # Laplacian: subtarct upscaled version of lower level from current level
+        # Laplacian: subtract upscaled version of lower level from current level
         # to get the high frequencies
         LA = np.subtract(gpA[i-1], cv2.pyrUp(gpA[i]))
         LB = np.subtract(gpB[i-1], cv2.pyrUp(gpB[i]))