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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) | |