postprocessing/rife/IFNet_HDv3.py

import torch
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")

backwarp_tenGrid = {}

def warp(tenInput, tenFlow, device):
    k = (str(tenFlow.device), str(tenFlow.size()))
    if k not in backwarp_tenGrid:
        tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
            1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
        tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
            1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
        backwarp_tenGrid[k] = torch.cat(
            [tenHorizontal, tenVertical], 1).to(device)

    tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
                         tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)

    g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=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)
    )

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. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
        flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / 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], device= flow.device)
            warped_img1 = warp(img1, flow[:, 2:4], device= flow.device)
            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