Updated RIFE CUDA to 3.1

Pkgs/rife-cuda/model/IFNet.py

@@ -1,115 +1,76 @@
 import torch
-import numpy as np
 import torch.nn as nn
 import torch.nn.functional as F
 from model.warplayer import warp
 
-
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
 
 def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
     return nn.Sequential(
         nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=False),
+                  padding=padding, dilation=dilation, bias=True),
-        nn.BatchNorm2d(out_planes),
+        )
-    )
-
 
 def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
     return nn.Sequential(
         nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=False),
+                  padding=padding, dilation=dilation, bias=True),
-        nn.BatchNorm2d(out_planes),
         nn.PReLU(out_planes)
     )
 
-
-class ResBlock(nn.Module):
-    def __init__(self, in_planes, out_planes, stride=1):
-        super(ResBlock, self).__init__()
-        if in_planes == out_planes and stride == 1:
-            self.conv0 = nn.Identity()
-        else:
-            self.conv0 = nn.Conv2d(in_planes, out_planes,
-                                   3, stride, 1, bias=False)
-        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
-        self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
-        self.relu1 = nn.PReLU(1)
-        self.relu2 = nn.PReLU(out_planes)
-        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
-        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
-
-    def forward(self, x):
-        y = self.conv0(x)
-        x = self.conv1(x)
-        x = self.conv2(x)
-        w = x.mean(3, True).mean(2, True)
-        w = self.relu1(self.fc1(w))
-        w = torch.sigmoid(self.fc2(w))
-        x = self.relu2(x * w + y)
-        return x
-
-
 class IFBlock(nn.Module):
     def __init__(self, in_planes, scale=1, c=64):
         super(IFBlock, self).__init__()
         self.scale = scale
-        self.conv0 = conv(in_planes, c, 3, 2, 1)
+        self.conv0 = nn.Sequential(
-        self.res0 = ResBlock(c, c)
+            conv(in_planes, c//2, 3, 2, 1),
-        self.res1 = ResBlock(c, c)
+            conv(c//2, c, 3, 2, 1),
-        self.res2 = ResBlock(c, c)
+            )
-        self.res3 = ResBlock(c, c)
+        self.convblock = nn.Sequential(
-        self.res4 = ResBlock(c, c)
+            conv(c, c),
-        self.res5 = ResBlock(c, c)
+            conv(c, c),
-        self.conv1 = nn.Conv2d(c, 8, 3, 1, 1)
+            conv(c, c),
-        self.up = nn.PixelShuffle(2)
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )
+        self.conv1 = nn.ConvTranspose2d(c, 4, 4, 2, 1)
 
     def forward(self, x):
         if self.scale != 1:
-            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
+            x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
-                              align_corners=False)
         x = self.conv0(x)
-        x = self.res0(x)
+        x = self.convblock(x) + x
-        x = self.res1(x)
-        x = self.res2(x)
-        x = self.res3(x)
-        x = self.res4(x)
-        x = self.res5(x)
         x = self.conv1(x)
-        flow = self.up(x)
+        flow = x
         if self.scale != 1:
-            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
+            flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
-                                 align_corners=False)
         return flow
     
-
 class IFNet(nn.Module):
     def __init__(self):
         super(IFNet, self).__init__()
-        self.block0 = IFBlock(6, scale=4, c=192)
+        self.block0 = IFBlock(6, scale=4, c=240)
-        self.block1 = IFBlock(8, scale=2, c=128)
+        self.block1 = IFBlock(10, scale=2, c=150)
-        self.block2 = IFBlock(8, scale=1, c=64)
+        self.block2 = IFBlock(10, scale=1, c=90)
 
     def forward(self, x):
-        x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
-                          align_corners=False)
         flow0 = self.block0(x)
         F1 = flow0
-        warped_img0 = warp(x[:, :3], F1)
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
-        warped_img1 = warp(x[:, 3:], -F1)
+        warped_img0 = warp(x[:, :3], F1_large[:, :2])
-        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
+        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
         F2 = (flow0 + flow1)
-        warped_img0 = warp(x[:, :3], F2)
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
-        warped_img1 = warp(x[:, 3:], -F2)
+        warped_img0 = warp(x[:, :3], F2_large[:, :2])
-        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
+        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
         F3 = (flow0 + flow1 + flow2)
         return F3, [F1, F2, F3]
-
-if __name__ == '__main__':
-    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
-    img1 = torch.tensor(np.random.normal(
-        0, 1, (3, 3, 256, 256))).float().to(device)
-    imgs = torch.cat((img0, img1), 1)
-    flownet = IFNet()
-    flow, _ = flownet(imgs)
-    print(flow.shape)

Pkgs/rife-cuda/model/IFNet15C.py

@@ -0,0 +1,76 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from model.warplayer import warp
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
+
+def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        )
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, scale=1, c=64):
+        super(IFBlock, self).__init__()
+        self.scale = scale
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c//2, 3, 2, 1),
+            conv(c//2, c, 3, 2, 1),
+            )
+        self.convblock = nn.Sequential(
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )
+        self.conv1 = nn.ConvTranspose2d(c, 4, 4, 2, 1)
+
+    def forward(self, x):
+        if self.scale != 1:
+            x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
+        x = self.conv0(x)
+        x = self.convblock(x) + x
+        x = self.conv1(x)
+        flow = x
+        if self.scale != 1:
+            flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
+        return flow
+    
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, scale=4, c=320)
+        self.block1 = IFBlock(10, scale=2, c=225)
+        self.block2 = IFBlock(10, scale=1, c=135)
+
+    def forward(self, x):
+        flow0 = self.block0(x)
+        F1 = flow0
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
+        warped_img0 = warp(x[:, :3], F1_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
+        F2 = (flow0 + flow1)
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
+        warped_img0 = warp(x[:, :3], F2_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
+        F3 = (flow0 + flow1 + flow2)
+        return F3, [F1, F2, F3]

Pkgs/rife-cuda/model/IFNet2F.py

@@ -1,115 +1,75 @@
 import torch
-import numpy as np
 import torch.nn as nn
 import torch.nn.functional as F
 from model.warplayer import warp
 
-
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
 
 def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
     return nn.Sequential(
         nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=False),
+                  padding=padding, dilation=dilation, bias=True),
-        nn.BatchNorm2d(out_planes),
+        )
-    )
-
 
 def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
     return nn.Sequential(
         nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=False),
+                  padding=padding, dilation=dilation, bias=True),
-        nn.BatchNorm2d(out_planes),
         nn.PReLU(out_planes)
     )
 
-
-class ResBlock(nn.Module):
-    def __init__(self, in_planes, out_planes, stride=1):
-        super(ResBlock, self).__init__()
-        if in_planes == out_planes and stride == 1:
-            self.conv0 = nn.Identity()
-        else:
-            self.conv0 = nn.Conv2d(in_planes, out_planes,
-                                   3, stride, 1, bias=False)
-        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
-        self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
-        self.relu1 = nn.PReLU(1)
-        self.relu2 = nn.PReLU(out_planes)
-        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
-        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
-
-    def forward(self, x):
-        y = self.conv0(x)
-        x = self.conv1(x)
-        x = self.conv2(x)
-        w = x.mean(3, True).mean(2, True)
-        w = self.relu1(self.fc1(w))
-        w = torch.sigmoid(self.fc2(w))
-        x = self.relu2(x * w + y)
-        return x
-
-
 class IFBlock(nn.Module):
     def __init__(self, in_planes, scale=1, c=64):
         super(IFBlock, self).__init__()
         self.scale = scale
-        self.conv0 = conv(in_planes, c, 3, 1, 1)
+        self.conv0 = nn.Sequential(
-        self.res0 = ResBlock(c, c)
+            conv(in_planes, c, 3, 2, 1),
-        self.res1 = ResBlock(c, c)
+            )
-        self.res2 = ResBlock(c, c)
+        self.convblock = nn.Sequential(
-        self.res3 = ResBlock(c, c)
+            conv(c, c),
-        self.res4 = ResBlock(c, c)
+            conv(c, c),
-        self.res5 = ResBlock(c, c)
+            conv(c, c),
-        self.conv1 = nn.Conv2d(c, 2, 3, 1, 1)
+            conv(c, c),
-        self.up = nn.PixelShuffle(2)
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )
+        self.conv1 = nn.Conv2d(c, 4, 3, 1, 1)
 
     def forward(self, x):
         if self.scale != 1:
-            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
+            x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
-                              align_corners=False)
         x = self.conv0(x)
-        x = self.res0(x)
+        x = self.convblock(x) + x
-        x = self.res1(x)
-        x = self.res2(x)
-        x = self.res3(x)
-        x = self.res4(x)
-        x = self.res5(x)
         x = self.conv1(x)
-        flow = x # self.up(x)
+        flow = x
         if self.scale != 1:
-            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
+            flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
-                                 align_corners=False)
         return flow
     
-
 class IFNet(nn.Module):
     def __init__(self):
         super(IFNet, self).__init__()
-        self.block0 = IFBlock(6, scale=4, c=192)
+        self.block0 = IFBlock(6, scale=4, c=240)
-        self.block1 = IFBlock(8, scale=2, c=128)
+        self.block1 = IFBlock(10, scale=2, c=150)
-        self.block2 = IFBlock(8, scale=1, c=64)
+        self.block2 = IFBlock(10, scale=1, c=90)
 
     def forward(self, x):
-        x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
-                          align_corners=False)
         flow0 = self.block0(x)
         F1 = flow0
-        warped_img0 = warp(x[:, :3], F1)
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
-        warped_img1 = warp(x[:, 3:], -F1)
+        warped_img0 = warp(x[:, :3], F1_large[:, :2])
-        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
+        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
         F2 = (flow0 + flow1)
-        warped_img0 = warp(x[:, :3], F2)
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
-        warped_img1 = warp(x[:, 3:], -F2)
+        warped_img0 = warp(x[:, :3], F2_large[:, :2])
-        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
+        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
         F3 = (flow0 + flow1 + flow2)
         return F3, [F1, F2, F3]
-
-if __name__ == '__main__':
-    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
-    img1 = torch.tensor(np.random.normal(
-        0, 1, (3, 3, 256, 256))).float().to(device)
-    imgs = torch.cat((img0, img1), 1)
-    flownet = IFNet()
-    flow, _ = flownet(imgs)
-    print(flow.shape)

Pkgs/rife-cuda/model/IFNet2F15C.py

@@ -1,115 +1,75 @@
 import torch
-import numpy as np
 import torch.nn as nn
 import torch.nn.functional as F
 from model.warplayer import warp
 
-
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
 
 def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
     return nn.Sequential(
         nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=False),
+                  padding=padding, dilation=dilation, bias=True),
-        nn.BatchNorm2d(out_planes),
+        )
-    )
-
 
 def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
     return nn.Sequential(
         nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=False),
+                  padding=padding, dilation=dilation, bias=True),
-        nn.BatchNorm2d(out_planes),
         nn.PReLU(out_planes)
     )
 
-
-class ResBlock(nn.Module):
-    def __init__(self, in_planes, out_planes, stride=1):
-        super(ResBlock, self).__init__()
-        if in_planes == out_planes and stride == 1:
-            self.conv0 = nn.Identity()
-        else:
-            self.conv0 = nn.Conv2d(in_planes, out_planes,
-                                   3, stride, 1, bias=False)
-        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
-        self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
-        self.relu1 = nn.PReLU(1)
-        self.relu2 = nn.PReLU(out_planes)
-        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
-        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
-
-    def forward(self, x):
-        y = self.conv0(x)
-        x = self.conv1(x)
-        x = self.conv2(x)
-        w = x.mean(3, True).mean(2, True)
-        w = self.relu1(self.fc1(w))
-        w = torch.sigmoid(self.fc2(w))
-        x = self.relu2(x * w + y)
-        return x
-
-
 class IFBlock(nn.Module):
     def __init__(self, in_planes, scale=1, c=64):
         super(IFBlock, self).__init__()
         self.scale = scale
-        self.conv0 = conv(in_planes, c, 3, 1, 1)
+        self.conv0 = nn.Sequential(
-        self.res0 = ResBlock(c, c)
+            conv(in_planes, c, 3, 2, 1),
-        self.res1 = ResBlock(c, c)
+            )
-        self.res2 = ResBlock(c, c)
+        self.convblock = nn.Sequential(
-        self.res3 = ResBlock(c, c)
+            conv(c, c),
-        self.res4 = ResBlock(c, c)
+            conv(c, c),
-        self.res5 = ResBlock(c, c)
+            conv(c, c),
-        self.conv1 = nn.Conv2d(c, 2, 3, 1, 1)
+            conv(c, c),
-        self.up = nn.PixelShuffle(2)
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )
+        self.conv1 = nn.Conv2d(c, 4, 3, 1, 1)
 
     def forward(self, x):
         if self.scale != 1:
-            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
+            x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
-                              align_corners=False)
         x = self.conv0(x)
-        x = self.res0(x)
+        x = self.convblock(x) + x
-        x = self.res1(x)
-        x = self.res2(x)
-        x = self.res3(x)
-        x = self.res4(x)
-        x = self.res5(x)
         x = self.conv1(x)
-        flow = x # self.up(x)
+        flow = x
         if self.scale != 1:
-            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
+            flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
-                                 align_corners=False)
         return flow
     
-
 class IFNet(nn.Module):
     def __init__(self):
         super(IFNet, self).__init__()
-        self.block0 = IFBlock(6, scale=4, c=288)
+        self.block0 = IFBlock(6, scale=4, c=360)
-        self.block1 = IFBlock(8, scale=2, c=192)
+        self.block1 = IFBlock(10, scale=2, c=225)
-        self.block2 = IFBlock(8, scale=1, c=96)
+        self.block2 = IFBlock(10, scale=1, c=135)
 
     def forward(self, x):
-        x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
-                          align_corners=False)
         flow0 = self.block0(x)
         F1 = flow0
-        warped_img0 = warp(x[:, :3], F1)
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
-        warped_img1 = warp(x[:, 3:], -F1)
+        warped_img0 = warp(x[:, :3], F1_large[:, :2])
-        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
+        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
         F2 = (flow0 + flow1)
-        warped_img0 = warp(x[:, :3], F2)
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
-        warped_img1 = warp(x[:, 3:], -F2)
+        warped_img0 = warp(x[:, :3], F2_large[:, :2])
-        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
+        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
         F3 = (flow0 + flow1 + flow2)
         return F3, [F1, F2, F3]
-
-if __name__ == '__main__':
-    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
-    img1 = torch.tensor(np.random.normal(
-        0, 1, (3, 3, 256, 256))).float().to(device)
-    imgs = torch.cat((img0, img1), 1)
-    flownet = IFNet()
-    flow, _ = flownet(imgs)
-    print(flow.shape)

Pkgs/rife-cuda/model/IFNet_HD.py

@@ -91,12 +91,9 @@ class IFNet(nn.Module):
         self.block2 = IFBlock(8, scale=2, c=96)
         self.block3 = IFBlock(8, scale=1, c=48)
 
-    def forward(self, x, UHD=False):
+    def forward(self, x, scale=1.0):
-        if UHD:
+        x = F.interpolate(x, scale_factor=0.5 * scale, mode="bilinear",
-            x = F.interpolate(x, scale_factor=0.25, mode="bilinear", align_corners=False)
+                          align_corners=False)
-        else:
-            x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
-                              align_corners=False)
         flow0 = self.block0(x)
         F1 = flow0
         warped_img0 = warp(x[:, :3], F1)
@@ -111,6 +108,8 @@ class IFNet(nn.Module):
         warped_img1 = warp(x[:, 3:], -F3)
         flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3), 1))
         F4 = (flow0 + flow1 + flow2 + flow3)
+        F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear",
+                           align_corners=False) / scale
         return F4, [F1, F2, F3, F4]
 
 if __name__ == '__main__':

Pkgs/rife-cuda/model/IFNet_HDv2.py

@@ -61,26 +61,28 @@ class IFNet(nn.Module):
         self.block2 = IFBlock(10, scale=2, c=96)
         self.block3 = IFBlock(10, scale=1, c=48)
 
-    def forward(self, x, UHD=False):
+    def forward(self, x, scale=1.0):
-        if UHD:
+        if scale != 1.0:
-            x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False)
+            x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False)
         flow0 = self.block0(x)
         F1 = flow0
-        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
         warped_img0 = warp(x[:, :3], F1_large[:, :2])
         warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
         flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
         F2 = (flow0 + flow1)
-        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
         warped_img0 = warp(x[:, :3], F2_large[:, :2])
         warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
         flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
         F3 = (flow0 + flow1 + flow2)
-        F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
+        F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
         warped_img0 = warp(x[:, :3], F3_large[:, :2])
         warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
         flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1))
         F4 = (flow0 + flow1 + flow2 + flow3)
+        if scale != 1.0:
+            F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear", align_corners=False) / scale
         return F4, [F1, F2, F3, F4]
 
 if __name__ == '__main__':

Pkgs/rife-cuda/model/IFNet_HDv3.py

@@ -0,0 +1,81 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from model.warplayer import warp
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
+
+def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        )
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64):
+        super(IFBlock, self).__init__()
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c, 3, 2, 1),
+            conv(c, 2*c, 3, 2, 1),
+            )
+        self.convblock0 = nn.Sequential(
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+        )
+        self.convblock1 = nn.Sequential(
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+        )
+        self.convblock2 = nn.Sequential(
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+        )
+        self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
+
+    def forward(self, x, flow=None, scale=1):
+        x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False)
+        if flow != None:
+            flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False) * (1. / scale)
+            x = torch.cat((x, flow), 1)
+        x = self.conv0(x)
+        x = self.convblock0(x) + x
+        x = self.convblock1(x) + x
+        x = self.convblock2(x) + x
+        x = self.conv1(x)
+        flow = x
+        if scale != 1:
+            flow = F.interpolate(flow, scale_factor= scale, mode="bilinear", align_corners=False) * scale
+        return flow
+    
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, c=80)
+        self.block1 = IFBlock(10, c=80)
+        self.block2 = IFBlock(10, c=80)
+
+    def forward(self, x, scale_list=[4,2,1]):
+        flow0 = self.block0(x, scale=scale_list[0])
+        F1 = flow0
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
+        warped_img0 = warp(x[:, :3], F1_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1), 1), F1_large, scale=scale_list[1])
+        F2 = (flow0 + flow1)
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
+        warped_img0 = warp(x[:, :3], F2_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1), 1), F2_large, scale=scale_list[2])
+        F3 = (flow0 + flow1 + flow2)
+        return F3, [F1, F2, F3]

Pkgs/rife-cuda/model/RIFE.py

@@ -34,29 +34,15 @@ def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilati
                   padding=padding, dilation=dilation, bias=True),
     )
 
-class ResBlock(nn.Module):
+class Conv2(nn.Module):
     def __init__(self, in_planes, out_planes, stride=2):
-        super(ResBlock, self).__init__()
+        super(Conv2, self).__init__()
-        if in_planes == out_planes and stride == 1:
-            self.conv0 = nn.Identity()
-        else:
-            self.conv0 = nn.Conv2d(in_planes, out_planes,
-                                   3, stride, 1, bias=False)
         self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
-        self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
-        self.relu1 = nn.PReLU(1)
-        self.relu2 = nn.PReLU(out_planes)
-        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
-        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
         
     def forward(self, x):
-        y = self.conv0(x)
         x = self.conv1(x)
         x = self.conv2(x)
-        w = x.mean(3, True).mean(2, True)
-        w = self.relu1(self.fc1(w))
-        w = torch.sigmoid(self.fc2(w))
-        x = self.relu2(x * w + y)
         return x
 
 c = 16
@@ -64,36 +50,32 @@ c = 16
 class ContextNet(nn.Module):
     def __init__(self):
         super(ContextNet, self).__init__()
-        self.conv1 = ResBlock(3, c)
+        self.conv1 = Conv2(3, c)
-        self.conv2 = ResBlock(c, 2*c)
+        self.conv2 = Conv2(c, 2*c)
-        self.conv3 = ResBlock(2*c, 4*c)
+        self.conv3 = Conv2(2*c, 4*c)
-        self.conv4 = ResBlock(4*c, 8*c)
+        self.conv4 = Conv2(4*c, 8*c)
 
     def forward(self, x, flow):
         x = self.conv1(x)
         f1 = warp(x, flow)
         x = self.conv2(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f2 = warp(x, flow)
         x = self.conv3(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f3 = warp(x, flow)
         x = self.conv4(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f4 = warp(x, flow)
         return [f1, f2, f3, f4]
 
-
 class FusionNet(nn.Module):
     def __init__(self):
         super(FusionNet, self).__init__()
-        self.down0 = ResBlock(8, 2*c)
+        self.down0 = Conv2(12, 2*c)
-        self.down1 = ResBlock(4*c, 4*c)
+        self.down1 = Conv2(4*c, 4*c)
-        self.down2 = ResBlock(8*c, 8*c)
+        self.down2 = Conv2(8*c, 8*c)
-        self.down3 = ResBlock(16*c, 16*c)
+        self.down3 = Conv2(16*c, 16*c)
         self.up0 = deconv(32*c, 8*c)
         self.up1 = deconv(16*c, 4*c)
         self.up2 = deconv(8*c, 2*c)
@@ -101,14 +83,14 @@ class FusionNet(nn.Module):
         self.conv = nn.Conv2d(c, 4, 3, 1, 1)
 
     def forward(self, img0, img1, flow, c0, c1, flow_gt):
-        warped_img0 = warp(img0, flow)
+        warped_img0 = warp(img0, flow[:, :2])
-        warped_img1 = warp(img1, -flow)
+        warped_img1 = warp(img1, flow[:, 2:4])
         if flow_gt == None:
             warped_img0_gt, warped_img1_gt = None, None
         else:
             warped_img0_gt = warp(img0, flow_gt[:, :2])
             warped_img1_gt = warp(img1, flow_gt[:, 2:4])
-        s0 = self.down0(torch.cat((warped_img0, warped_img1, flow), 1))
+        s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
         s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
         s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
         s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
@@ -119,7 +101,6 @@ class FusionNet(nn.Module):
         x = self.conv(x)
         return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
 
-
 class Model:
     def __init__(self, local_rank=-1):
         self.flownet = IFNet()
@@ -129,12 +110,13 @@ class Model:
         self.optimG = AdamW(itertools.chain(
             self.flownet.parameters(),
             self.contextnet.parameters(),
-            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
         self.schedulerG = optim.lr_scheduler.CyclicLR(
             self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
         self.epe = EPE()
         self.ter = Ternary()
         self.sobel = SOBEL()
+        # self.vgg = VGGPerceptualLoss().to(device)
         if local_rank != -1:
             self.flownet = DDP(self.flownet, device_ids=[
                                local_rank], output_device=local_rank)
@@ -158,7 +140,7 @@ class Model:
         self.contextnet.to(device)
         self.fusionnet.to(device)
 
-    def load_model(self, path, rank):
+    def load_model(self, path, rank=-1):
         def convert(param):
             if rank == -1:
                 return {
@@ -185,8 +167,8 @@ class Model:
     def predict(self, imgs, flow, training=True, flow_gt=None):
         img0 = imgs[:, :3]
         img1 = imgs[:, 3:]
-        c0 = self.contextnet(img0, flow)
+        c0 = self.contextnet(img0, flow[:, :2])
-        c1 = self.contextnet(img1, -flow)
+        c1 = self.contextnet(img1, flow[:, 2:4])
         flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
                              align_corners=False) * 2.0
         refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
@@ -203,7 +185,7 @@ class Model:
 
     def inference(self, img0, img1):
         imgs = torch.cat((img0, img1), 1)
-        flow, _ = self.flownet(imgs)
+        flow, _ = self.flownet(torch.cat((img0, img1), 1))
         return self.predict(imgs, flow, training=False)
 
     def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
@@ -228,8 +210,8 @@ class Model:
                                          align_corners=False) * 0.5).detach()
             loss_cons = 0
             for i in range(3):
-                loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
+                loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
-                loss_cons += self.epe(-flow_list[i], flow_gt[:, 2:4], 1)
+                loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
             loss_cons = loss_cons.mean() * 0.01
         else:
             loss_cons = torch.tensor([0])
@@ -239,6 +221,7 @@ class Model:
         if training:
             self.optimG.zero_grad()
             loss_G = loss_l1 + loss_cons + loss_ter
+            # loss_G = self.vgg(pred, gt) + loss_cons + loss_ter
             loss_G.backward()
             self.optimG.step()
         return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask

Pkgs/rife-cuda/model/RIFE15C.py

@@ -0,0 +1,235 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.optim import AdamW
+import torch.optim as optim
+import itertools
+from model.warplayer import warp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from model.IFNet15C import *
+import torch.nn.functional as F
+from model.loss import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
+                                 kernel_size=4, stride=2, padding=1, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+    )
+
+class Conv2(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=2):
+        super(Conv2, self).__init__()
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
+        
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+c = 24
+
+class ContextNet(nn.Module):
+    def __init__(self):
+        super(ContextNet, self).__init__()
+        self.conv1 = Conv2(3, c)
+        self.conv2 = Conv2(c, 2*c)
+        self.conv3 = Conv2(2*c, 4*c)
+        self.conv4 = Conv2(4*c, 8*c)
+
+    def forward(self, x, flow):
+        x = self.conv1(x)
+        f1 = warp(x, flow)
+        x = self.conv2(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f2 = warp(x, flow)
+        x = self.conv3(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f3 = warp(x, flow)
+        x = self.conv4(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f4 = warp(x, flow)
+        return [f1, f2, f3, f4]
+
+class FusionNet(nn.Module):
+    def __init__(self):
+        super(FusionNet, self).__init__()
+        self.down0 = Conv2(12, 2*c)
+        self.down1 = Conv2(4*c, 4*c)
+        self.down2 = Conv2(8*c, 8*c)
+        self.down3 = Conv2(16*c, 16*c)
+        self.up0 = deconv(32*c, 8*c)
+        self.up1 = deconv(16*c, 4*c)
+        self.up2 = deconv(8*c, 2*c)
+        self.up3 = deconv(4*c, c)
+        self.conv = nn.Conv2d(c, 4, 3, 1, 1)
+
+    def forward(self, img0, img1, flow, c0, c1, flow_gt):
+        warped_img0 = warp(img0, flow[:, :2])
+        warped_img1 = warp(img1, flow[:, 2:4])
+        if flow_gt == None:
+            warped_img0_gt, warped_img1_gt = None, None
+        else:
+            warped_img0_gt = warp(img0, flow_gt[:, :2])
+            warped_img1_gt = warp(img1, flow_gt[:, 2:4])
+        s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
+        s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+        s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+        s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+        x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+        x = self.up1(torch.cat((x, s2), 1))
+        x = self.up2(torch.cat((x, s1), 1))
+        x = self.up3(torch.cat((x, s0), 1))
+        x = self.conv(x)
+        return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+
+class Model:
+    def __init__(self, local_rank=-1):
+        self.flownet = IFNet()
+        self.contextnet = ContextNet()
+        self.fusionnet = FusionNet()
+        self.device()
+        self.optimG = AdamW(itertools.chain(
+            self.flownet.parameters(),
+            self.contextnet.parameters(),
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
+        self.schedulerG = optim.lr_scheduler.CyclicLR(
+            self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
+        self.epe = EPE()
+        self.ter = Ternary()
+        self.sobel = SOBEL()
+        if local_rank != -1:
+            self.flownet = DDP(self.flownet, device_ids=[
+                               local_rank], output_device=local_rank)
+            self.contextnet = DDP(self.contextnet, device_ids=[
+                                  local_rank], output_device=local_rank)
+            self.fusionnet = DDP(self.fusionnet, device_ids=[
+                                 local_rank], output_device=local_rank)
+
+    def train(self):
+        self.flownet.train()
+        self.contextnet.train()
+        self.fusionnet.train()
+
+    def eval(self):
+        self.flownet.eval()
+        self.contextnet.eval()
+        self.fusionnet.eval()
+
+    def device(self):
+        self.flownet.to(device)
+        self.contextnet.to(device)
+        self.fusionnet.to(device)
+
+    def load_model(self, path, rank=-1):
+        def convert(param):
+            if rank == -1:
+                return {
+                    k.replace("module.", ""): v
+                    for k, v in param.items()
+                    if "module." in k
+                }
+            else:
+                return param
+        if rank <= 0:
+            self.flownet.load_state_dict(
+                convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
+            self.contextnet.load_state_dict(
+                convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
+            self.fusionnet.load_state_dict(
+                convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
+
+    def save_model(self, path, rank):
+        if rank == 0:
+            torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
+            torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
+            torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
+
+    def predict(self, imgs, flow, training=True, flow_gt=None):
+        img0 = imgs[:, :3]
+        img1 = imgs[:, 3:]
+        c0 = self.contextnet(img0, flow[:, :2])
+        c1 = self.contextnet(img1, flow[:, 2:4])
+        flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
+                             align_corners=False) * 2.0
+        refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
+            img0, img1, flow, c0, c1, flow_gt)
+        res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
+        mask = torch.sigmoid(refine_output[:, 3:4])
+        merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
+        pred = merged_img + res
+        pred = torch.clamp(pred, 0, 1)
+        if training:
+            return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+        else:
+            return pred
+
+    def inference(self, img0, img1):
+        imgs = torch.cat((img0, img1), 1)
+        flow, _ = self.flownet(imgs)
+        return self.predict(imgs, flow, training=False)
+
+    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
+        for param_group in self.optimG.param_groups:
+            param_group['lr'] = learning_rate
+        if training:
+            self.train()
+        else:
+            self.eval()
+        flow, flow_list = self.flownet(imgs)
+        pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
+            imgs, flow, flow_gt=flow_gt)
+        loss_ter = self.ter(pred, gt).mean()
+        if training:
+            with torch.no_grad():
+                loss_flow = torch.abs(warped_img0_gt - gt).mean()
+                loss_mask = torch.abs(
+                    merged_img - gt).sum(1, True).float().detach()
+                loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
+                                          align_corners=False).detach()
+                flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
+                                         align_corners=False) * 0.5).detach()
+            loss_cons = 0
+            for i in range(3):
+                loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
+                loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
+            loss_cons = loss_cons.mean() * 0.01
+        else:
+            loss_cons = torch.tensor([0])
+            loss_flow = torch.abs(warped_img0 - gt).mean()
+            loss_mask = 1
+        loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
+        if training:
+            self.optimG.zero_grad()
+            loss_G = loss_l1 + loss_cons + loss_ter
+            loss_G.backward()
+            self.optimG.step()
+        return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
+
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    imgs = torch.cat((img0, img1), 1)
+    model = Model()
+    model.eval()
+    print(model.inference(imgs).shape)

Pkgs/rife-cuda/model/RIFE2F.py

@@ -34,29 +34,15 @@ def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilati
                   padding=padding, dilation=dilation, bias=True),
     )
 
-class ResBlock(nn.Module):
+class Conv2(nn.Module):
     def __init__(self, in_planes, out_planes, stride=2):
-        super(ResBlock, self).__init__()
+        super(Conv2, self).__init__()
-        if in_planes == out_planes and stride == 1:
-            self.conv0 = nn.Identity()
-        else:
-            self.conv0 = nn.Conv2d(in_planes, out_planes,
-                                   3, stride, 1, bias=False)
         self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
-        self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
-        self.relu1 = nn.PReLU(1)
-        self.relu2 = nn.PReLU(out_planes)
-        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
-        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
         
     def forward(self, x):
-        y = self.conv0(x)
         x = self.conv1(x)
         x = self.conv2(x)
-        w = x.mean(3, True).mean(2, True)
-        w = self.relu1(self.fc1(w))
-        w = torch.sigmoid(self.fc2(w))
-        x = self.relu2(x * w + y)
         return x
 
 c = 16
@@ -64,36 +50,32 @@ c = 16
 class ContextNet(nn.Module):
     def __init__(self):
         super(ContextNet, self).__init__()
-        self.conv1 = ResBlock(3, c, 1)
+        self.conv1 = Conv2(3, c, 1)
-        self.conv2 = ResBlock(c, 2*c)
+        self.conv2 = Conv2(c, 2*c)
-        self.conv3 = ResBlock(2*c, 4*c)
+        self.conv3 = Conv2(2*c, 4*c)
-        self.conv4 = ResBlock(4*c, 8*c)
+        self.conv4 = Conv2(4*c, 8*c)
 
     def forward(self, x, flow):
         x = self.conv1(x)
         f1 = warp(x, flow)
         x = self.conv2(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f2 = warp(x, flow)
         x = self.conv3(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f3 = warp(x, flow)
         x = self.conv4(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f4 = warp(x, flow)
         return [f1, f2, f3, f4]
 
-
 class FusionNet(nn.Module):
     def __init__(self):
         super(FusionNet, self).__init__()
-        self.down0 = ResBlock(8, 2*c, 1)
+        self.down0 = Conv2(12, 2*c, 1)
-        self.down1 = ResBlock(4*c, 4*c)
+        self.down1 = Conv2(4*c, 4*c)
-        self.down2 = ResBlock(8*c, 8*c)
+        self.down2 = Conv2(8*c, 8*c)
-        self.down3 = ResBlock(16*c, 16*c)
+        self.down3 = Conv2(16*c, 16*c)
         self.up0 = deconv(32*c, 8*c)
         self.up1 = deconv(16*c, 4*c)
         self.up2 = deconv(8*c, 2*c)
@@ -101,14 +83,14 @@ class FusionNet(nn.Module):
         self.conv = nn.Conv2d(c, 4, 3, 2, 1)
 
     def forward(self, img0, img1, flow, c0, c1, flow_gt):
-        warped_img0 = warp(img0, flow)
+        warped_img0 = warp(img0, flow[:, :2])
-        warped_img1 = warp(img1, -flow)
+        warped_img1 = warp(img1, flow[:, 2:4])
         if flow_gt == None:
             warped_img0_gt, warped_img1_gt = None, None
         else:
             warped_img0_gt = warp(img0, flow_gt[:, :2])
             warped_img1_gt = warp(img1, flow_gt[:, 2:4])
-        s0 = self.down0(torch.cat((warped_img0, warped_img1, flow), 1))
+        s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
         s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
         s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
         s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
@@ -119,7 +101,6 @@ class FusionNet(nn.Module):
         x = self.conv(x)
         return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
 
-
 class Model:
     def __init__(self, local_rank=-1):
         self.flownet = IFNet()
@@ -129,7 +110,7 @@ class Model:
         self.optimG = AdamW(itertools.chain(
             self.flownet.parameters(),
             self.contextnet.parameters(),
-            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
         self.schedulerG = optim.lr_scheduler.CyclicLR(
             self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
         self.epe = EPE()
@@ -158,14 +139,17 @@ class Model:
         self.contextnet.to(device)
         self.fusionnet.to(device)
 
-    def load_model(self, path, rank=0):
+    def load_model(self, path, rank=-1):
         def convert(param):
-            return {
+            if rank == -1:
-                k.replace("module.", ""): v
+                return {
-                for k, v in param.items()
+                    k.replace("module.", ""): v
-                if "module." in k
+                    for k, v in param.items()
-            }
+                    if "module." in k
-        if rank == 0:
+                }
+            else:
+                return param
+        if rank <= 0:
             self.flownet.load_state_dict(
                 convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
             self.contextnet.load_state_dict(
@@ -173,21 +157,19 @@ class Model:
             self.fusionnet.load_state_dict(
                 convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
 
-    def save_model(self, path, rank=0):
+    def save_model(self, path, rank):
         if rank == 0:
-            torch.save(self.flownet.state_dict(),
+            torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
-                       '{}/flownet.pkl'.format(path))
+            torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
-            torch.save(self.contextnet.state_dict(),
-                       '{}/contextnet.pkl'.format(path))
             torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
 
     def predict(self, imgs, flow, training=True, flow_gt=None):
         img0 = imgs[:, :3]
         img1 = imgs[:, 3:]
         flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
-	                     align_corners=False) * 2.0
+                             align_corners=False) * 2.0
-        c0 = self.contextnet(img0, flow)
+        c0 = self.contextnet(img0, flow[:, :2])
-        c1 = self.contextnet(img1, -flow)
+        c1 = self.contextnet(img1, flow[:, 2:4])
         refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
             img0, img1, flow, c0, c1, flow_gt)
         res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
@@ -201,9 +183,8 @@ class Model:
             return pred
 
     def inference(self, img0, img1):
-        with torch.no_grad():
+        imgs = torch.cat((img0, img1), 1)
-            imgs = torch.cat((img0, img1), 1)
+        flow, _ = self.flownet(imgs)
-            flow, _ = self.flownet(imgs)
         return self.predict(imgs, flow, training=False)
 
     def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
@@ -222,10 +203,14 @@ class Model:
                 loss_flow = torch.abs(warped_img0_gt - gt).mean()
                 loss_mask = torch.abs(
                     merged_img - gt).sum(1, True).float().detach()
+                loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
+                                          align_corners=False).detach()
+                flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
+                                         align_corners=False) * 0.5).detach()
             loss_cons = 0
             for i in range(3):
-                loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
+                loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
-                loss_cons += self.epe(-flow_list[i], flow_gt[:, 2:4], 1)
+                loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
             loss_cons = loss_cons.mean() * 0.01
         else:
             loss_cons = torch.tensor([0])

Pkgs/rife-cuda/model/RIFE2F15C.py

@@ -34,29 +34,15 @@ def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilati
                   padding=padding, dilation=dilation, bias=True),
     )
 
-class ResBlock(nn.Module):
+class Conv2(nn.Module):
     def __init__(self, in_planes, out_planes, stride=2):
-        super(ResBlock, self).__init__()
+        super(Conv2, self).__init__()
-        if in_planes == out_planes and stride == 1:
-            self.conv0 = nn.Identity()
-        else:
-            self.conv0 = nn.Conv2d(in_planes, out_planes,
-                                   3, stride, 1, bias=False)
         self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
-        self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
-        self.relu1 = nn.PReLU(1)
-        self.relu2 = nn.PReLU(out_planes)
-        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
-        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
         
     def forward(self, x):
-        y = self.conv0(x)
         x = self.conv1(x)
         x = self.conv2(x)
-        w = x.mean(3, True).mean(2, True)
-        w = self.relu1(self.fc1(w))
-        w = torch.sigmoid(self.fc2(w))
-        x = self.relu2(x * w + y)
         return x
 
 c = 24
@@ -64,36 +50,32 @@ c = 24
 class ContextNet(nn.Module):
     def __init__(self):
         super(ContextNet, self).__init__()
-        self.conv1 = ResBlock(3, c, 1)
+        self.conv1 = Conv2(3, c, 1)
-        self.conv2 = ResBlock(c, 2*c)
+        self.conv2 = Conv2(c, 2*c)
-        self.conv3 = ResBlock(2*c, 4*c)
+        self.conv3 = Conv2(2*c, 4*c)
-        self.conv4 = ResBlock(4*c, 8*c)
+        self.conv4 = Conv2(4*c, 8*c)
 
     def forward(self, x, flow):
         x = self.conv1(x)
         f1 = warp(x, flow)
         x = self.conv2(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f2 = warp(x, flow)
         x = self.conv3(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f3 = warp(x, flow)
         x = self.conv4(x)
-        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
-                             align_corners=False) * 0.5
         f4 = warp(x, flow)
         return [f1, f2, f3, f4]
 
-
 class FusionNet(nn.Module):
     def __init__(self):
         super(FusionNet, self).__init__()
-        self.down0 = ResBlock(8, 2*c, 1)
+        self.down0 = Conv2(12, 2*c, 1)
-        self.down1 = ResBlock(4*c, 4*c)
+        self.down1 = Conv2(4*c, 4*c)
-        self.down2 = ResBlock(8*c, 8*c)
+        self.down2 = Conv2(8*c, 8*c)
-        self.down3 = ResBlock(16*c, 16*c)
+        self.down3 = Conv2(16*c, 16*c)
         self.up0 = deconv(32*c, 8*c)
         self.up1 = deconv(16*c, 4*c)
         self.up2 = deconv(8*c, 2*c)
@@ -101,14 +83,14 @@ class FusionNet(nn.Module):
         self.conv = nn.Conv2d(c, 4, 3, 2, 1)
 
     def forward(self, img0, img1, flow, c0, c1, flow_gt):
-        warped_img0 = warp(img0, flow)
+        warped_img0 = warp(img0, flow[:, :2])
-        warped_img1 = warp(img1, -flow)
+        warped_img1 = warp(img1, flow[:, 2:4])
         if flow_gt == None:
             warped_img0_gt, warped_img1_gt = None, None
         else:
             warped_img0_gt = warp(img0, flow_gt[:, :2])
             warped_img1_gt = warp(img1, flow_gt[:, 2:4])
-        s0 = self.down0(torch.cat((warped_img0, warped_img1, flow), 1))
+        s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
         s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
         s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
         s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
@@ -119,7 +101,6 @@ class FusionNet(nn.Module):
         x = self.conv(x)
         return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
 
-
 class Model:
     def __init__(self, local_rank=-1):
         self.flownet = IFNet()
@@ -129,7 +110,7 @@ class Model:
         self.optimG = AdamW(itertools.chain(
             self.flownet.parameters(),
             self.contextnet.parameters(),
-            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
         self.schedulerG = optim.lr_scheduler.CyclicLR(
             self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
         self.epe = EPE()
@@ -158,14 +139,17 @@ class Model:
         self.contextnet.to(device)
         self.fusionnet.to(device)
 
-    def load_model(self, path, rank=0):
+    def load_model(self, path, rank=-1):
         def convert(param):
-            return {
+            if rank == -1:
-                k.replace("module.", ""): v
+                return {
-                for k, v in param.items()
+                    k.replace("module.", ""): v
-                if "module." in k
+                    for k, v in param.items()
-            }
+                    if "module." in k
-        if rank == 0:
+                }
+            else:
+                return param
+        if rank <= 0:
             self.flownet.load_state_dict(
                 convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
             self.contextnet.load_state_dict(
@@ -173,21 +157,19 @@ class Model:
             self.fusionnet.load_state_dict(
                 convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
 
-    def save_model(self, path, rank=0):
+    def save_model(self, path, rank):
         if rank == 0:
-            torch.save(self.flownet.state_dict(),
+            torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
-                       '{}/flownet.pkl'.format(path))
+            torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
-            torch.save(self.contextnet.state_dict(),
-                       '{}/contextnet.pkl'.format(path))
             torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
 
     def predict(self, imgs, flow, training=True, flow_gt=None):
         img0 = imgs[:, :3]
         img1 = imgs[:, 3:]
         flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
-	                     align_corners=False) * 2.0
+                             align_corners=False) * 2.0
-        c0 = self.contextnet(img0, flow)
+        c0 = self.contextnet(img0, flow[:, :2])
-        c1 = self.contextnet(img1, -flow)
+        c1 = self.contextnet(img1, flow[:, 2:4])
         refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
             img0, img1, flow, c0, c1, flow_gt)
         res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
@@ -201,9 +183,8 @@ class Model:
             return pred
 
     def inference(self, img0, img1):
-        with torch.no_grad():
+        imgs = torch.cat((img0, img1), 1)
-            imgs = torch.cat((img0, img1), 1)
+        flow, _ = self.flownet(imgs)
-            flow, _ = self.flownet(imgs)
         return self.predict(imgs, flow, training=False)
 
     def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
@@ -222,10 +203,14 @@ class Model:
                 loss_flow = torch.abs(warped_img0_gt - gt).mean()
                 loss_mask = torch.abs(
                     merged_img - gt).sum(1, True).float().detach()
+                loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
+                                          align_corners=False).detach()
+                flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
+                                         align_corners=False) * 0.5).detach()
             loss_cons = 0
             for i in range(3):
-                loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
+                loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
-                loss_cons += self.epe(-flow_list[i], flow_gt[:, 2:4], 1)
+                loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
             loss_cons = loss_cons.mean() * 0.01
         else:
             loss_cons = torch.tensor([0])

Pkgs/rife-cuda/model/RIFE_HD.py

@@ -135,7 +135,7 @@ class Model:
         self.optimG = AdamW(itertools.chain(
             self.flownet.parameters(),
             self.contextnet.parameters(),
-            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
         self.schedulerG = optim.lr_scheduler.CyclicLR(
             self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
         self.epe = EPE()
@@ -188,11 +188,9 @@ class Model:
             torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
             torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
 
-    def predict(self, imgs, flow, training=True, flow_gt=None, UHD=False):
+    def predict(self, imgs, flow, training=True, flow_gt=None):
         img0 = imgs[:, :3]
         img1 = imgs[:, 3:]
-        if UHD:
-            flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
         c0 = self.contextnet(img0, flow)
         c1 = self.contextnet(img1, -flow)
         flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
@@ -209,10 +207,10 @@ class Model:
         else:
             return pred
 
-    def inference(self, img0, img1, UHD=False):
+    def inference(self, img0, img1, scale=1.0):
         imgs = torch.cat((img0, img1), 1)
-        flow, _ = self.flownet(imgs, UHD)
+        flow, _ = self.flownet(imgs, scale)
-        return self.predict(imgs, flow, training=False, UHD=UHD)
+        return self.predict(imgs, flow, training=False)
 
     def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
         for param_group in self.optimG.param_groups:

Pkgs/rife-cuda/model/RIFE_HDv2.py

@@ -120,7 +120,7 @@ class Model:
         self.optimG = AdamW(itertools.chain(
             self.flownet.parameters(),
             self.contextnet.parameters(),
-            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
         self.schedulerG = optim.lr_scheduler.CyclicLR(
             self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
         self.epe = EPE()
@@ -173,11 +173,9 @@ class Model:
             torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
             torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
 
-    def predict(self, imgs, flow, training=True, flow_gt=None, UHD=False):
+    def predict(self, imgs, flow, training=True, flow_gt=None):
         img0 = imgs[:, :3]
         img1 = imgs[:, 3:]
-        if UHD:
-            flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
         c0 = self.contextnet(img0, flow[:, :2])
         c1 = self.contextnet(img1, flow[:, 2:4])
         flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
@@ -194,10 +192,10 @@ class Model:
         else:
             return pred
 
-    def inference(self, img0, img1, UHD=False):
+    def inference(self, img0, img1, scale=1.0):
         imgs = torch.cat((img0, img1), 1)
-        flow, _ = self.flownet(imgs, UHD)
+        flow, _ = self.flownet(imgs, scale)
-        return self.predict(imgs, flow, training=False, UHD=UHD)
+        return self.predict(imgs, flow, training=False)
 
     def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
         for param_group in self.optimG.param_groups:

Pkgs/rife-cuda/model/RIFE_HDv3.py

@@ -0,0 +1,249 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.optim import AdamW
+import torch.optim as optim
+import itertools
+from model.warplayer import warp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from model.IFNet_HDv3 import *
+import torch.nn.functional as F
+from model.loss import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
+                                 kernel_size=4, stride=2, padding=1, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+    )
+
+class Conv2(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=2):
+        super(Conv2, self).__init__()
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+c = 32
+
+class ContextNet(nn.Module):
+    def __init__(self):
+        super(ContextNet, self).__init__()
+        self.conv0 = Conv2(3, c)
+        self.conv1 = Conv2(c, c)
+        self.conv2 = Conv2(c, 2*c)
+        self.conv3 = Conv2(2*c, 4*c)
+        self.conv4 = Conv2(4*c, 8*c)
+
+    def forward(self, x, flow):
+        x = self.conv0(x)
+        x = self.conv1(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f1 = warp(x, flow)
+        x = self.conv2(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f2 = warp(x, flow)
+        x = self.conv3(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f3 = warp(x, flow)
+        x = self.conv4(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f4 = warp(x, flow)
+        return [f1, f2, f3, f4]
+
+
+class FusionNet(nn.Module):
+    def __init__(self):
+        super(FusionNet, self).__init__()
+        self.conv0 = Conv2(10, c)
+        self.down0 = Conv2(c, 2*c)
+        self.down1 = Conv2(4*c, 4*c)
+        self.down2 = Conv2(8*c, 8*c)
+        self.down3 = Conv2(16*c, 16*c)
+        self.up0 = deconv(32*c, 8*c)
+        self.up1 = deconv(16*c, 4*c)
+        self.up2 = deconv(8*c, 2*c)
+        self.up3 = deconv(4*c, c)
+        self.conv = nn.ConvTranspose2d(c, 4, 4, 2, 1)
+
+    def forward(self, img0, img1, flow, c0, c1, flow_gt):
+        warped_img0 = warp(img0, flow[:, :2])
+        warped_img1 = warp(img1, flow[:, 2:4])
+        if flow_gt == None:
+            warped_img0_gt, warped_img1_gt = None, None
+        else:
+            warped_img0_gt = warp(img0, flow_gt[:, :2])
+            warped_img1_gt = warp(img1, flow_gt[:, 2:4])
+        x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1))
+        s0 = self.down0(x)
+        s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+        s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+        s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+        x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+        x = self.up1(torch.cat((x, s2), 1))
+        x = self.up2(torch.cat((x, s1), 1))
+        x = self.up3(torch.cat((x, s0), 1))
+        x = self.conv(x)
+        return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+
+
+class Model:
+    def __init__(self, local_rank=-1):
+        self.flownet = IFNet()
+        self.contextnet = ContextNet()
+        self.fusionnet = FusionNet()
+        self.device()
+        self.optimG = AdamW(itertools.chain(
+            self.flownet.parameters(),
+            self.contextnet.parameters(),
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
+        self.schedulerG = optim.lr_scheduler.CyclicLR(
+            self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
+        self.epe = EPE()
+        self.ter = Ternary()
+        self.sobel = SOBEL()
+        if local_rank != -1:
+            self.flownet = DDP(self.flownet, device_ids=[
+                               local_rank], output_device=local_rank)
+            self.contextnet = DDP(self.contextnet, device_ids=[
+                                  local_rank], output_device=local_rank)
+            self.fusionnet = DDP(self.fusionnet, device_ids=[
+                                 local_rank], output_device=local_rank)
+
+    def train(self):
+        self.flownet.train()
+        self.contextnet.train()
+        self.fusionnet.train()
+
+    def eval(self):
+        self.flownet.eval()
+        self.contextnet.eval()
+        self.fusionnet.eval()
+
+    def device(self):
+        self.flownet.to(device)
+        self.contextnet.to(device)
+        self.fusionnet.to(device)
+
+    def load_model(self, path, rank):
+        def convert(param):
+            if rank == -1:
+                return {
+                    k.replace("module.", ""): v
+                    for k, v in param.items()
+                    if "module." in k
+                }
+            else:
+                return param
+        if rank <= 0:
+            self.flownet.load_state_dict(
+                convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
+            self.contextnet.load_state_dict(
+                convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
+            self.fusionnet.load_state_dict(
+                convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
+
+    def save_model(self, path, rank):
+        if rank == 0:
+            torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
+            torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
+            torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
+
+    def predict(self, imgs, flow, training=True, flow_gt=None, UHD=False):
+        img0 = imgs[:, :3]
+        img1 = imgs[:, 3:]
+        if UHD:
+            flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0        
+        c0 = self.contextnet(img0, flow[:, :2])
+        c1 = self.contextnet(img1, flow[:, 2:4])
+        flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
+                             align_corners=False) * 2.0
+        refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
+            img0, img1, flow, c0, c1, flow_gt)
+        res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
+        mask = torch.sigmoid(refine_output[:, 3:4])
+        merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
+        pred = merged_img + res
+        pred = torch.clamp(pred, 0, 1)
+        if training:
+            return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+        else:
+            return pred
+
+    def inference(self, img0, img1, UHD=False):
+        imgs = torch.cat((img0, img1), 1)
+        scale_list = [8, 4, 2]
+        flow, _ = self.flownet(imgs, scale_list)
+        res = self.predict(imgs, flow, training=False, UHD=False)
+        return res
+
+    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
+        for param_group in self.optimG.param_groups:
+            param_group['lr'] = learning_rate
+        if training:
+            self.train()
+        else:
+            self.eval()
+        flow, flow_list = self.flownet(imgs)
+        pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
+            imgs, flow, flow_gt=flow_gt)
+        loss_ter = self.ter(pred, gt).mean()
+        if training:
+            with torch.no_grad():
+                loss_flow = torch.abs(warped_img0_gt - gt).mean()
+                loss_mask = torch.abs(
+                    merged_img - gt).sum(1, True).float().detach()
+                loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
+                                          align_corners=False).detach()
+                flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
+                                         align_corners=False) * 0.5).detach()
+            loss_cons = 0
+            for i in range(4):
+                loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
+                loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
+            loss_cons = loss_cons.mean() * 0.01
+        else:
+            loss_cons = torch.tensor([0])
+            loss_flow = torch.abs(warped_img0 - gt).mean()
+            loss_mask = 1
+        loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
+        if training:
+            self.optimG.zero_grad()
+            loss_G = loss_l1 + loss_cons + loss_ter
+            loss_G.backward()
+            self.optimG.step()
+        return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
+
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    imgs = torch.cat((img0, img1), 1)
+    model = Model()
+    model.eval()
+    print(model.inference(imgs).shape)

Pkgs/rife-cuda/model/loss.py

@@ -2,6 +2,7 @@ import torch
 import numpy as np
 import torch.nn as nn
 import torch.nn.functional as F
+import torchvision.models as models
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -79,6 +80,45 @@ class SOBEL(nn.Module):
         loss = (L1X+L1Y)
         return loss
 
+class MeanShift(nn.Conv2d):
+    def __init__(self, data_mean, data_std, data_range=1, norm=True):
+        c = len(data_mean)
+        super(MeanShift, self).__init__(c, c, kernel_size=1)
+        std = torch.Tensor(data_std)
+        self.weight.data = torch.eye(c).view(c, c, 1, 1)
+        if norm:
+            self.weight.data.div_(std.view(c, 1, 1, 1))
+            self.bias.data = -1 * data_range * torch.Tensor(data_mean)
+            self.bias.data.div_(std)
+        else:
+            self.weight.data.mul_(std.view(c, 1, 1, 1))
+            self.bias.data = data_range * torch.Tensor(data_mean)
+        self.requires_grad = False
+            
+class VGGPerceptualLoss(torch.nn.Module):
+    def __init__(self, rank=0):
+        super(VGGPerceptualLoss, self).__init__()
+        blocks = []
+        pretrained = True
+        self.vgg_pretrained_features = models.vgg19(pretrained=pretrained).features
+        self.normalize = MeanShift([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], norm=True).cuda()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, X, Y, indices=None):
+        X = self.normalize(X)
+        Y = self.normalize(Y)
+        indices = [2, 7, 12, 21, 30]
+        weights = [1.0/2.6, 1.0/4.8, 1.0/3.7, 1.0/5.6, 10/1.5]
+        k = 0
+        loss = 0
+        for i in range(indices[-1]):
+            X = self.vgg_pretrained_features[i](X)
+            Y = self.vgg_pretrained_features[i](Y)
+            if (i+1) in indices:
+                loss += weights[k] * (X - Y.detach()).abs().mean() * 0.1
+                k += 1
+        return loss
 
 if __name__ == '__main__':
     img0 = torch.zeros(3, 3, 256, 256).float().to(device)

Pkgs/rife-cuda/model/warplayer.py

@@ -19,4 +19,4 @@ def warp(tenInput, tenFlow):
                          tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
 
     g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
-    return torch.nn.functional.grid_sample(input=tenInput, grid=torch.clamp(g, -1, 1), mode='bilinear', padding_mode='zeros', align_corners=True)
+    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)

Pkgs/rife-cuda/models.json

@@ -18,6 +18,11 @@
 	"name": "RIFE 3.0",
 	"desc": "Latest General Model",
 	"dir": "RIFE30",
+},
+{
+	"name": "RIFE 3.1",
+	"desc": "Latest General Model",
+	"dir": "RIFE31",
 	"isDefault": "true"
 }
 ]

Pkgs/rife-cuda/rife.py

@@ -36,7 +36,6 @@ parser.add_argument('--exp', dest='exp', type=int, default=1)
 args = parser.parse_args()
 assert (not args.input is None)
 
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 torch.set_grad_enabled(False)
 if torch.cuda.is_available():
@@ -56,13 +55,21 @@ except:
     print("Failed to get hardware info!")
 
 try:
+    try:
+        from model.RIFE_HDv2 import Model
+        model = Model()
+        model.load_model(os.path.join(dname, args.model), -1)
+        print("Loaded v2.x HD model.")
+    except:
+        from model.RIFE_HDv3 import Model
+        model = Model()
+        model.load_model(os.path.join(dname, args.model), -1)
+        print("Loaded v3.x HD model.")
+except:
     from model.RIFE_HD import Model
     model = Model()
     model.load_model(os.path.join(dname, args.model), -1)
-except:
+    print("Loaded v1.x HD model")
-    from model.RIFE_HDv2 import Model
-    model = Model()
-    model.load_model(os.path.join(dname, args.model), -1)
 model.eval()
 model.device()