Mirrors/ECCV2022-RIFE
1
0
Fork 0
mirror of https://github.com/hzwer/ECCV2022-RIFE.git synced 2025-12-16 08:27:45 +01:00
Code Issues Packages Projects Releases Wiki Activity

Update scale mode

Browse Source
This commit is contained in:
hzwer
2021-02-27 19:40:53 +08:00
parent b989b4571d
commit f75bbacbd1
1 changed files with 8 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
model/IFNet_HDv2.py
View File

@@ -39,15 +39,16 @@ class IFBlock(nn.Module):
)
self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
def forward(self, x):
if self.scale != 1:
def forward(self, x, scale=1.0):
scale = self.scale / scale
if scale != 1.0:
x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
align_corners=False)
x = self.conv0(x)
x = self.convblock(x)
x = self.conv1(x)
flow = x
if self.scale != 1:
if scale != 1.0:
flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
align_corners=False)
return flow
@@ -62,27 +63,23 @@ class IFNet(nn.Module):
self.block3 = IFBlock(10, scale=1, c=48)
def forward(self, x, scale=1.0):
if scale != 1.0:
x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False)
flow0 = self.block0(x)
flow0 = self.block0(x, scale)
F1 = flow0
F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=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))
flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1), scale)
F2 = (flow0 + flow1)
F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=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))
flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1), scale)
F3 = (flow0 + flow1 + flow2)
F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=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))
flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1), scale)
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__':