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import torch
import torch.nn as nn
import torch.nn.functional as F
from model.warplayer import warp
from model.refine import *
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
return nn.Sequential(
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
nn.PReLU(out_planes)
)
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, bias=True),
nn.PReLU(out_planes)
)
class IFBlock(nn.Module):
def __init__(self, in_planes, c=64):
super(IFBlock, self).__init__()
self.conv0 = nn.Sequential(
conv(in_planes, c//2, 3, 2, 1),
conv(c//2, c, 3, 2, 1),
)
self.convblock = nn.Sequential(
conv(c, c),
conv(c, c),
conv(c, c),
conv(c, c),
conv(c, c),
conv(c, c),
conv(c, c),
conv(c, c),
)
self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
def forward(self, x, flow, scale):
if scale != 1:
x = F.interpolate(x, scale_factor = 1. / scale, mode="bilinear", align_corners=False)
if flow != None:
flow = F.interpolate(flow, scale_factor = 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
x = torch.cat((x, flow), 1)
x = self.conv0(x)
x = self.convblock(x) + x
tmp = self.lastconv(x)
tmp = F.interpolate(tmp, scale_factor = scale * 2, mode="bilinear", align_corners=False)
flow = tmp[:, :4] * scale * 2
mask = tmp[:, 4:5]
return flow, mask
class IFNet_m(nn.Module):
def __init__(self):
super(IFNet_m, self).__init__()
self.block0 = IFBlock(6+1, c=240)
self.block1 = IFBlock(13+4+1, c=150)
self.block2 = IFBlock(13+4+1, c=90)
self.block_tea = IFBlock(16+4+1, c=90)
self.contextnet = Contextnet()
self.unet = Unet()
def forward(self, x, scale=[4,2,1], timestep=0.5, returnflow=False):
timestep = (x[:, :1].clone() * 0 + 1) * timestep
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, timestep, 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, timestep), 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, timestep, 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]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
if returnflow:
return flow
else:
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
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