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,
scale_list=[4, 2, 1],
training=False,
ensemble=True,
ada=True):
channel = x.shape[1] // 2
img0 = x[:, :channel]
img1 = x[:, channel:]
flow_list = []
merged = []
mask_list = []
warped_img0 = img0
warped_img1 = img1
flow = torch.zeros_like(x[:, :4]).to(device)
mask = torch.zeros_like(x[:, :1]).to(device)
loss_cons = 0
block = [self.block0, self.block1, self.block2]
for i in range(3):
f0, m0 = block[i](torch.cat(
(warped_img0[:, :3], warped_img1[:, :3], mask), 1),
flow,
scale=scale_list[i])
if ensemble:
f1, m1 = block[i](torch.cat(
(warped_img1[:, :3], warped_img0[:, :3], -mask), 1),
torch.cat((flow[:, 2:4], flow[:, :2]), 1),
scale=scale_list[i])
f0 = (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
m0 = (m0 + (-m1)) / 2
flow = flow + f0 # TODO all dark frame output in 3.8 model
mask = mask + m0
mask_list.append(mask)
flow_list.append(flow)
warped_img0 = warp(img0, flow[:, :2])
warped_img1 = warp(img1, flow[:, 2:4])
merged.append((warped_img0, warped_img1))
'''
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[:, 1:4] * 2 - 1
'''
mask_list[2] = torch.sigmoid(mask_list[2])
merged[2] = merged[2][0] * mask_list[2] + merged[2][1] * (1 -
mask_list[2])
return merged, flow
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])
x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1))
s0 = self.down0(x)
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, scale=1.0, ensemble=False, ada=True):
if scale != 1.0:
x = F.interpolate(x,
scale_factor=scale,
mode="bilinear",
align_corners=False)
flow0 = self.block0(torch.cat((x[:, :3], x[:, 3:]), 1))
if ensemble:
flow01 = self.block0(torch.cat((x[:, 3:], x[:, :3]), 1))
flow0 = (flow0 + torch.cat((flow01[:, 2:4], flow01[:, :2]), 1)) / 2
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))
if ensemble:
F1_large = torch.cat((F1_large[:, 2:4], F1_large[:, :2]), 1)
flow11 = self.block1(
torch.cat((warped_img1, warped_img0, F1_large), 1))
flow1 = (flow1 + torch.cat((flow11[:, 2:4], flow11[:, :2]), 1)) / 2
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))
if ensemble:
F2_large = torch.cat((F2_large[:, 2:4], F2_large[:, :2]), 1)
flow21 = self.block2(
torch.cat((warped_img1, warped_img0, F2_large), 1))
flow2 = (flow2 + torch.cat((flow21[:, 2:4], flow21[:, :2]), 1)) / 2
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))
if ensemble:
F3_large = torch.cat((F3_large[:, 2:4], F3_large[:, :2]), 1)
flow31 = self.block3(
torch.cat((warped_img1, warped_img0, F3_large), 1))
flow3 = (flow3 + torch.cat((flow31[:, 2:4], flow31[:, :2]), 1)) / 2
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], ensemble=False):
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_d0, mask_d0 = stu[i](torch.cat(
(img0, img1, warped_img0, warped_img1, mask), 1),
flow,
scale=scale[i])
if ensemble:
flow_d1, mask_d1 = stu[i](torch.cat(
(img1, img0, warped_img1, warped_img0, -mask), 1),
torch.cat(
(flow[:, 2:4], flow[:, :2]),
1),
scale=scale[i])
flow_d0 = (flow_d0 + torch.cat(
(flow_d1[:, 2:4], flow_d1[:, :2]), 1)) / 2
mask_d0 = (mask_d0 + (-mask_d1)) / 2
flow = flow + flow_d0
mask = mask + mask_d0
else:
flow, mask = stu[i](torch.cat((img0, img1), 1),
None,
scale=scale[i])
if ensemble:
flow1, mask1 = stu[i](torch.cat((img1, img0), 1),
None,
scale=scale[i])
flow = (flow + torch.cat(
(flow1[:, 2:4], flow1[:, :2]), 1)) / 2
mask = (mask + (-mask1)) / 2
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