|
import torch |
|
import torch.nn as nn |
|
from .warplayer import warp |
|
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) |
|
|
|
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) |
|
|
