mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2025-12-16 16:37:51 +01:00
@@ -39,16 +39,15 @@ class IFBlock(nn.Module):
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)
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)
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self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
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self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
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def forward(self, x, scale=1.0):
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def forward(self, x):
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scale = self.scale / scale
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if self.scale != 1:
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if scale != 1.0:
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x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
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x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
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align_corners=False)
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align_corners=False)
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x = self.conv0(x)
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x = self.conv0(x)
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x = self.convblock(x)
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x = self.convblock(x)
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x = self.conv1(x)
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x = self.conv1(x)
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flow = x
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flow = x
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if scale != 1.0:
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
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flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
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align_corners=False)
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align_corners=False)
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return flow
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return flow
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@@ -63,23 +62,27 @@ class IFNet(nn.Module):
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self.block3 = IFBlock(10, scale=1, c=48)
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self.block3 = IFBlock(10, scale=1, c=48)
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def forward(self, x, scale=1.0):
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def forward(self, x, scale=1.0):
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flow0 = self.block0(x, scale)
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if scale != 1.0:
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x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False)
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flow0 = self.block0(x)
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F1 = flow0
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F1 = flow0
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F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
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F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
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warped_img0 = warp(x[:, :3], F1_large[:, :2])
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warped_img0 = warp(x[:, :3], F1_large[:, :2])
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warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
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warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1), scale)
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
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F2 = (flow0 + flow1)
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F2 = (flow0 + flow1)
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F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
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F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
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warped_img0 = warp(x[:, :3], F2_large[:, :2])
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warped_img0 = warp(x[:, :3], F2_large[:, :2])
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warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
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warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1), scale)
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
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F3 = (flow0 + flow1 + flow2)
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F3 = (flow0 + flow1 + flow2)
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F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
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F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
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warped_img0 = warp(x[:, :3], F3_large[:, :2])
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warped_img0 = warp(x[:, :3], F3_large[:, :2])
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warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
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warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
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flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1), scale)
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flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1))
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F4 = (flow0 + flow1 + flow2 + flow3)
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F4 = (flow0 + flow1 + flow2 + flow3)
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if scale != 1.0:
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F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear", align_corners=False) / scale
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return F4, [F1, F2, F3, F4]
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return F4, [F1, F2, F3, F4]
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if __name__ == '__main__':
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if __name__ == '__main__':
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