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import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model.warplayer import warp
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def conv_wo_act(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),
)
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=False),
nn.BatchNorm2d(out_planes),
nn.PReLU(out_planes)
)
class ResBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride=1):
super(ResBlock, self).__init__()
if in_planes == out_planes and stride == 1:
self.conv0 = nn.Identity()
else:
self.conv0 = nn.Conv2d(in_planes, out_planes,
3, stride, 1, bias=False)
self.conv1 = conv(in_planes, out_planes, 5, stride, 2)
self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
self.relu1 = nn.PReLU(1)
self.relu2 = nn.PReLU(out_planes)
self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
def forward(self, x):
y = self.conv0(x)
x = self.conv1(x)
x = self.conv2(x)
w = x.mean(3, True).mean(2, True)
w = self.relu1(self.fc1(w))
w = torch.sigmoid(self.fc2(w))
x = self.relu2(x * w + y)
return x
class IFBlock(nn.Module):
def __init__(self, in_planes, scale=1, c=64):
super(IFBlock, self).__init__()
self.scale = scale
self.conv0 = conv(in_planes, c, 5, 2, 2)
self.res0 = ResBlock(c, c)
self.res1 = ResBlock(c, c)
self.res2 = ResBlock(c, c)
self.res3 = ResBlock(c, c)
self.res4 = ResBlock(c, c)
self.res5 = ResBlock(c, c)
self.conv1 = nn.Conv2d(c, 8, 3, 1, 1)
self.up = nn.PixelShuffle(2)
def forward(self, x):
if self.scale != 1:
x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
align_corners=False)
x = self.conv0(x)
x = self.res0(x)
x = self.res1(x)
x = self.res2(x)
x = self.res3(x)
x = self.res4(x)
x = self.res5(x)
x = self.conv1(x)
flow = self.up(x)
if self.scale != 1:
flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
align_corners=False)
return flow
class IFNet(nn.Module):
def __init__(self):
super(IFNet, self).__init__()
self.block0 = IFBlock(6, scale=8, c=192)
self.block1 = IFBlock(8, scale=4, c=128)
self.block2 = IFBlock(8, scale=2, c=96)
self.block3 = IFBlock(8, scale=1, c=48)
def forward(self, x, scale=1.0):
x = F.interpolate(x, scale_factor=0.5 * scale, mode="bilinear",
align_corners=False)
flow0 = self.block0(x)
F1 = flow0
warped_img0 = warp(x[:, :3], F1)
warped_img1 = warp(x[:, 3:], -F1)
flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
F2 = (flow0 + flow1)
warped_img0 = warp(x[:, :3], F2)
warped_img1 = warp(x[:, 3:], -F2)
flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
F3 = (flow0 + flow1 + flow2)
warped_img0 = warp(x[:, :3], F3)
warped_img1 = warp(x[:, 3:], -F3)
flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3), 1))
F4 = (flow0 + flow1 + flow2 + flow3)
F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear",
align_corners=False) / scale
return F4, [F1, F2, F3, F4]
if __name__ == '__main__':
img0 = torch.zeros(3, 3, 256, 256).float().to(device)
img1 = torch.tensor(np.random.normal(
0, 1, (3, 3, 256, 256))).float().to(device)
imgs = torch.cat((img0, img1), 1)
flownet = IFNet()
flow, _ = flownet(imgs)
print(flow.shape)
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