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flowframes
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Revert broken RIFE v1/v2 code

This commit is contained in:
N00MKRAD 2021-05-18 09:21:02 +02:00
parent 7b17644fd0
commit 1da13688c4
4 changed files with 26 additions and 23 deletions
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11
Pkgs/rife-cuda/model/IFNet_HD.py
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@ -91,9 +91,12 @@ class IFNet(nn.Module):
self.block2 = IFBlock(8, scale=2, c=96)
self.block3 = IFBlock(8, scale=1, c=48)
def forward(self, x, scale=1.0):
x = F.interpolate(x, scale_factor=0.5 * scale, mode="bilinear",
align_corners=False)
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)
flow0 = self.block0(x)
F1 = flow0
warped_img0 = warp(x[:, :3], F1)
@ -108,8 +111,6 @@ 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__':

14
Pkgs/rife-cuda/model/IFNet_HDv2.py
View File

@ -61,28 +61,26 @@ class IFNet(nn.Module):
self.block2 = IFBlock(10, scale=2, c=96)
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)
def forward(self, x, UHD=False):
if UHD:
x = F.interpolate(x, scale_factor=0.5, 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) * 2.0
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))
F2 = (flow0 + flow1)
F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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))
F3 = (flow0 + flow1 + flow2)
F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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))
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__':

12
Pkgs/rife-cuda/model/RIFE_HD.py
View File

@ -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-4)
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()
@ -188,9 +188,11 @@ 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):
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)
c1 = self.contextnet(img1, -flow)
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
@ -207,10 +209,10 @@ class Model:
else:
return pred
def inference(self, img0, img1, scale=1.0):
def inference(self, img0, img1, UHD=False):
imgs = torch.cat((img0, img1), 1)
flow, _ = self.flownet(imgs, scale)
return self.predict(imgs, flow, training=False)
flow, _ = self.flownet(imgs, UHD)
return self.predict(imgs, flow, training=False, UHD=UHD)
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
for param_group in self.optimG.param_groups:

12
Pkgs/rife-cuda/model/RIFE_HDv2.py
View File

@ -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-4)
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()
@ -173,9 +173,11 @@ 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):
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",
@ -192,10 +194,10 @@ class Model:
else:
return pred
def inference(self, img0, img1, scale=1.0):
def inference(self, img0, img1, UHD=False):
imgs = torch.cat((img0, img1), 1)
flow, _ = self.flownet(imgs, scale)
return self.predict(imgs, flow, training=False)
flow, _ = self.flownet(imgs, UHD)
return self.predict(imgs, flow, training=False, UHD=UHD)
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
for param_group in self.optimG.param_groups: