| 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) |
|
|
