align_faces.py

@@ -36,7 +36,7 @@ def _umeyama(src, dst, estimate_scale=True, scale=1.0):
     -------
     T : (N + 1, N + 1)
         The homogeneous similarity transformation matrix. The matrix contains
-        NaN values only if the problem==not well-conditioned.
+        NaN values only if the problem is not well-conditioned.
     References
     ----------
     .. [1] "Least-squares estimation of transformation parameters between two

face_model/face_gan.py

@@ -29,7 +29,7 @@ class FaceGAN(object):
         else:
             self.model = FullGenerator_SR(self.in_resolution, self.out_resolution, 512, self.n_mlp, channel_multiplier, narrow=narrow, device=self.device)
         pretrained_dict = torch.load(self.mfile, map_location=torch.device('cpu'))
-        if self.key==not None: pretrained_dict = pretrained_dict[self.key]
+        if self.key is not None: pretrained_dict = pretrained_dict[self.key]
         self.model.load_state_dict(pretrained_dict)
         self.model.to(self.device)
         self.model.eval()

face_model/model.py

@@ -370,7 +370,7 @@ class ToRGB(nn.Module):
         out = self.conv(input, style)
         out = out + self.bias
 
-        if skip==not None:
+        if skip is not None:
             skip = self.upsample(skip)
 
             out = out + skip

retinaface/data/wider_face.py

@@ -71,7 +71,7 @@ class WiderFaceDetection(data.Dataset):
 
             annotations = np.append(annotations, annotation, axis=0)
         target = np.array(annotations)
-        if self.preproc==not None:
+        if self.preproc is not None:
             img, target = self.preproc(img, target)
 
         return torch.from_numpy(img), target