Update pintar.py

pintar.py

@@ -29,6 +29,8 @@ def Normalize(inputs):
     l = inputs[:, :, 0:1]
     ab = inputs[:, :, 1:3]
     l = l - 50
+    l = l / 50  # Normalizar L al rango [-1, 1]
+    ab = ab / 110  # Normalizar ab al rango [-1, 1]
     lab = np.concatenate((l, ab), 2)
     return lab.astype('float32')
 
@@ -77,12 +79,12 @@ if __name__ == "__main__":
                 img_lab = img_lab.to(device).unsqueeze(0)
 
                 with torch.no_grad():
-                    img_resize = F.interpolate(img_lab / 110., size=(256, 256), mode='bilinear', recompute_scale_factor=False, align_corners=False)
-                    img_L_resize = F.interpolate(img_resize[:, :1, :, :] / 50., size=(256, 256), mode='bilinear', recompute_scale_factor=False, align_corners=False)
+                    img_resize = F.interpolate(img_lab, size=(256, 256), mode='bilinear', align_corners=False)
+                    img_L_resize = F.interpolate(img_resize[:, :1, :, :], size=(256, 256), mode='bilinear', align_corners=False)
 
                     color_vector = colorEncoder(img_resize)
                     fake_ab = colorUNet((img_L_resize, color_vector))
-                    fake_ab = F.interpolate(fake_ab, size=(img.shape[0], img.shape[1]), mode='bilinear', recompute_scale_factor=False, align_corners=False)
+                    fake_ab = F.interpolate(fake_ab, size=(img.shape[0], img.shape[1]), mode='bilinear', align_corners=False)
 
                     fake_img = torch.cat((img_lab[:, :1, :, :], fake_ab), 1)
                     fake_img = Lab2RGB_out(fake_img)