Merge remote-tracking branch 'origin/main' into main

# Conflicts:
#	model/RIFE.py

README.md

@@ -35,7 +35,7 @@ cd arXiv2020-RIFE
 pip3 install -r requirements.txt
 ```
 
-* Download the pretrained **HD** models from [here](https://drive.google.com/file/d/10-2AaFUyX-c7yCfubsxF2NTvM7DgvS8l/view?usp=sharing). (百度网盘链接:https://pan.baidu.com/s/1cJ7-dPuwR8THPUGWb207ZQ 密码:aa0w，把压缩包解开后放在 train_log/\*)
+* Download the pretrained **HD** models from [here](https://drive.google.com/file/d/1APIzVeI-4ZZCEuIRE1m6WYfSCaOsi_7_/view?usp=sharing). (百度网盘链接:https://pan.baidu.com/share/init?surl=u6Q7-i4Hu4Vx9_5BJibPPA 密码:hfk3，把压缩包解开后放在 train_log/\*)
 
 * Unzip and move the pretrained parameters to train_log/\*
 

inference_video.py

@@ -213,7 +213,7 @@ while True:
     I1 = pad_image(I1)
     I0_small = F.interpolate(I0, (32, 32), mode='bilinear', align_corners=False)
     I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
-    ssim = ssim_matlab(I0_small, I1_small)
+    ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
 
     if ssim > 0.995:
         if skip_frame % 100 == 0:

model/RIFE.py

@@ -57,7 +57,15 @@ class Model:
             return pred, merged
         else:
             return pred
+<<<<<<< HEAD
     '''
+=======
+
+    def inference(self, img0, img1, scale=None):
+        imgs = torch.cat((img0, img1), 1)
+        flow, _ = self.flownet(torch.cat((img0, img1), 1))
+        return self.predict(imgs, flow, training=False)
+>>>>>>> origin/main
 
     def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
         for param_group in self.optimG.param_groups: