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import torch
import torch.nn as nn
import torch.nn.functional as F
from .warplayer import warp
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 conv_bn(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=False,
),
nn.BatchNorm2d(out_planes),
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.convblock0 = nn.Sequential(conv(c, c), conv(c, c))
self.convblock1 = nn.Sequential(conv(c, c), conv(c, c))
self.convblock2 = nn.Sequential(conv(c, c), conv(c, c))
self.convblock3 = nn.Sequential(conv(c, c), conv(c, c))
self.conv1 = nn.Sequential(
nn.ConvTranspose2d(c, c // 2, 4, 2, 1),
nn.PReLU(c // 2),
nn.ConvTranspose2d(c // 2, 4, 4, 2, 1),
)
self.conv2 = nn.Sequential(
nn.ConvTranspose2d(c, c // 2, 4, 2, 1),
nn.PReLU(c // 2),
nn.ConvTranspose2d(c // 2, 1, 4, 2, 1),
)
def forward(self, x, flow, scale=1):
x = F.interpolate(
x, scale_factor=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False
)
flow = (
F.interpolate(
flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False
)
* 1.0
/ scale
)
feat = self.conv0(torch.cat((x, flow), 1))
feat = self.convblock0(feat) + feat
feat = self.convblock1(feat) + feat
feat = self.convblock2(feat) + feat
feat = self.convblock3(feat) + feat
flow = self.conv1(feat)
mask = self.conv2(feat)
flow = (
F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
* scale
)
mask = F.interpolate(
mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False
)
return flow, mask
class IFNet(nn.Module):
def __init__(self):
super(IFNet, self).__init__()
self.block0 = IFBlock(7 + 4, c=90)
self.block1 = IFBlock(7 + 4, c=90)
self.block2 = IFBlock(7 + 4, c=90)
self.block_tea = IFBlock(10 + 4, c=90)
# self.contextnet = Contextnet()
# self.unet = Unet()
def forward(self, x, scale_list=[4, 2, 1], training=False):
if training == False:
channel = x.shape[1] // 2
img0 = x[:, :channel]
img1 = x[:, channel:]
flow_list = []
merged = []
mask_list = []
warped_img0 = img0
warped_img1 = img1
flow = (x[:, :4]).detach() * 0
mask = (x[:, :1]).detach() * 0
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])
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],
)
flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
mask = mask + (m0 + (-m1)) / 2
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
"""
for i in range(3):
mask_list[i] = torch.sigmoid(mask_list[i])
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
# merged[i] = torch.clamp(merged[i] + res, 0, 1)
return flow_list, mask_list[2], merged
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