Mirrors/ECCV2022-RIFE
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Update UHD mode

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hzwer
2021-02-27 16:57:47 +08:00
parent 830cdbe3ac
commit a3ba6a19d3
4 changed files with 19 additions and 25 deletions
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11
model/IFNet_HD.py
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@@ -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):
if UHD:
x = F.interpolate(x, scale_factor=0.25, mode="bilinear", align_corners=False)
else:
x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
align_corners=False)
def forward(self, x, scale=1.0):
x = F.interpolate(x, scale_factor=0.5 * scale, 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__':

10
model/RIFE_HD.py
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@@ -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)
return self.predict(imgs, flow, training=False, UHD=UHD)
flow, _ = self.flownet(imgs, scale)
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:

10
model/RIFE_HDv2.py
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@@ -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)
return self.predict(imgs, flow, training=False, UHD=UHD)
flow, _ = self.flownet(imgs, scale)
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: