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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=True),
)
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, scale=1, c=64):
super(IFBlock, self).__init__()
self.scale = scale
self.conv0 = nn.Sequential(
conv(in_planes, c, 3, 2, 1),
conv(c, 2*c, 3, 2, 1),
)
self.convblock = nn.Sequential(
conv(2*c, 2*c),
conv(2*c, 2*c),
conv(2*c, 2*c),
conv(2*c, 2*c),
conv(2*c, 2*c),
conv(2*c, 2*c),
)
self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
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.convblock(x)
x = self.conv1(x)
flow = 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(10, scale=4, c=128)
self.block2 = IFBlock(10, scale=2, c=96)
self.block3 = IFBlock(10, scale=1, c=48)
def forward(self, x, scale=1.0):
if scale != 1.0:
x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False)
flow0 = self.block0(x)
F1 = flow0
F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
warped_img0 = warp(x[:, :3], F1_large[:, :2])
warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
F2 = (flow0 + flow1)
F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
warped_img0 = warp(x[:, :3], F2_large[:, :2])
warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
F3 = (flow0 + flow1 + flow2)
F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
warped_img0 = warp(x[:, :3], F3_large[:, :2])
warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1))
F4 = (flow0 + flow1 + flow2 + flow3)
if scale != 1.0:
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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