import torch import torch.nn as nn import torch.nn.functional as F from .warplayer import warp device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 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=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False ) flow = ( F.interpolate( flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False ) * 1.0 / 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]) warped_img1 = warp(img1, flow[:, 2:4]) 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