Add 2R model
This commit is contained in:
hzwer
parent
7a1cf7bfa5
commit
f8f415bd8e
|
|
@ -0,0 +1,108 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from model.warplayer import warp
|
||||
from model.refine_2R 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, 1, 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, mode="bilinear", align_corners=False)
|
||||
flow = tmp[:, :4] * scale
|
||||
mask = tmp[:, 4:5]
|
||||
return flow, mask
|
||||
|
||||
class IFNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(IFNet, self).__init__()
|
||||
self.block0 = IFBlock(6, c=240)
|
||||
self.block1 = IFBlock(13+4, c=150)
|
||||
self.block2 = IFBlock(13+4, c=90)
|
||||
self.block_tea = IFBlock(16+4, c=90)
|
||||
self.contextnet = Contextnet()
|
||||
self.unet = Unet()
|
||||
|
||||
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]) ** 2).mean(1, True) ** 0.5 * 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
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
import torch.optim as optim
|
||||
import itertools
|
||||
from model.warplayer import warp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
import torch.nn.functional as F
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
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)
|
||||
)
|
||||
|
||||
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, bias=True),
|
||||
nn.PReLU(out_planes)
|
||||
)
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
c = 16
|
||||
class Contextnet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Contextnet, self).__init__()
|
||||
self.conv1 = Conv2(3, c, 1)
|
||||
self.conv2 = Conv2(c, 2*c)
|
||||
self.conv3 = Conv2(2*c, 4*c)
|
||||
self.conv4 = Conv2(4*c, 8*c)
|
||||
|
||||
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]
|
||||
|
||||
class Unet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Unet, self).__init__()
|
||||
self.down0 = Conv2(17, 2*c, 1)
|
||||
self.down1 = Conv2(4*c, 4*c)
|
||||
self.down2 = Conv2(8*c, 8*c)
|
||||
self.down3 = Conv2(16*c, 16*c)
|
||||
self.up0 = deconv(32*c, 8*c)
|
||||
self.up1 = deconv(16*c, 4*c)
|
||||
self.up2 = deconv(8*c, 2*c)
|
||||
self.up3 = deconv(4*c, c)
|
||||
self.conv = nn.Conv2d(c, 3, 3, 2, 1)
|
||||
|
||||
def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 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 torch.sigmoid(x)
|
||||
Loading…
Reference in New Issue