def forward(self, x, flow):
x = self.conv1(x)
flow = F.interpolate(flow,
scale_factor=0.5,
mode="bilinear",
align_corners=False,
recompute_scale_factor=False) * 0.5
f1 = warp(x, flow)
x = self.conv2(x)
flow = F.interpolate(flow,
scale_factor=0.5,
mode="bilinear",
align_corners=False,
recompute_scale_factor=False) * 0.5
f2 = warp(x, flow)
x = self.conv3(x)
flow = F.interpolate(flow,
scale_factor=0.5,
mode="bilinear",
align_corners=False,
recompute_scale_factor=False) * 0.5
f3 = warp(x, flow)
x = self.conv4(x)
flow = F.interpolate(flow,
scale_factor=0.5,
mode="bilinear",
align_corners=False,
recompute_scale_factor=False) * 0.5
f4 = warp(x, flow)
return [f1, f2, f3, f4]
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]
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, self.cFlag)
warped_img1 = warp(x[:, 3:], -F1, self.cFlag)
flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
F2 = (flow0 + flow1)
warped_img0 = warp(x[:, :3], F2, self.cFlag)
warped_img1 = warp(x[:, 3:], -F2, self.cFlag)
flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
F3 = (flow0 + flow1 + flow2)
return F3, [F1, F2, F3]
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
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)
warped_img1 = warp(x[:, 3:], -F1)
flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
F2 = (flow0 + flow1)
warped_img0 = warp(x[:, :3], F2)
warped_img1 = warp(x[:, 3:], -F2)
flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
F3 = (flow0 + flow1 + flow2)
warped_img0 = warp(x[:, :3], F3)
warped_img1 = warp(x[:, 3:], -F3)
flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3), 1))
F4 = (flow0 + flow1 + flow2 + flow3)
return F4, [F1, F2, F3, F4]
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)
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)
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]
def forward(self, x, scale=[4, 2, 1], timestep=0.5):
img0 = x[:, :3]
img1 = x[:, 3:6]
gt = x[:, 6:] # In inference time, gt is None
flow_list = []
merged = []
mask_list = []
warped_img0 = img0
warped_img1 = img1
flow = None
loss_distill = 0
stu = [self.block0, self.block1, self.block2]
for i in range(3):
if flow != None:
flow_d, mask_d = stu[i](torch.cat(
(img0, img1, warped_img0, warped_img1, mask), 1),
flow,
scale=scale[i])
flow = flow + flow_d
mask = mask + mask_d
else:
flow, mask = stu[i](torch.cat((img0, img1), 1),
None,
scale=scale[i])
mask_list.append(torch.sigmoid(mask))
flow_list.append(flow)
warped_img0 = warp(img0, flow[:, :2])
warped_img1 = warp(img1, flow[:, 2:4])
merged_student = (warped_img0, warped_img1)
merged.append(merged_student)
if gt.shape[1] == 3:
flow_d, mask_d = self.block_tea(torch.cat(
(img0, img1, warped_img0, warped_img1, mask, gt), 1),
flow,
scale=1)
flow_teacher = flow + flow_d
warped_img0_teacher = warp(img0, flow_teacher[:, :2])
warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
mask_teacher = torch.sigmoid(mask + mask_d)
merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (
1 - mask_teacher)
else:
flow_teacher = None
merged_teacher = None
for i in range(3):
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (
1 - mask_list[i])
if gt.shape[1] == 3:
loss_mask = ((merged[i] - gt).abs().mean(1, True) >
(merged_teacher - gt).abs().mean(1, True) +
0.01).float().detach()
loss_distill += ((flow_teacher.detach() - flow_list[i]).abs() *
loss_mask).mean()
c0 = self.contextnet(img0, flow[:, :2])
c1 = self.contextnet(img1, flow[:, 2:4])
tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0,
c1)
res = tmp[:, :3] * 2 - 1
merged[2] = torch.clamp(merged[2] + res, 0, 1)
return flow_list, mask_list[
2], merged, flow_teacher, merged_teacher, loss_distill
