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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from .warplayer import warp |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): |
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return nn.Sequential( |
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nn.Conv2d( |
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in_planes, |
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out_planes, |
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kernel_size=kernel_size, |
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stride=stride, |
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padding=padding, |
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dilation=dilation, |
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bias=True, |
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), |
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nn.PReLU(out_planes), |
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) |
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def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): |
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return nn.Sequential( |
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nn.Conv2d( |
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in_planes, |
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out_planes, |
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kernel_size=kernel_size, |
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stride=stride, |
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padding=padding, |
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dilation=dilation, |
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bias=False, |
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), |
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nn.BatchNorm2d(out_planes), |
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nn.PReLU(out_planes), |
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) |
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class IFBlock(nn.Module): |
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def __init__(self, in_planes, c=64): |
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super(IFBlock, self).__init__() |
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self.conv0 = nn.Sequential( |
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conv(in_planes, c // 2, 3, 2, 1), |
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conv(c // 2, c, 3, 2, 1), |
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) |
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self.convblock0 = nn.Sequential(conv(c, c), conv(c, c)) |
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self.convblock1 = nn.Sequential(conv(c, c), conv(c, c)) |
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self.convblock2 = nn.Sequential(conv(c, c), conv(c, c)) |
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self.convblock3 = nn.Sequential(conv(c, c), conv(c, c)) |
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self.conv1 = nn.Sequential( |
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nn.ConvTranspose2d(c, c // 2, 4, 2, 1), |
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nn.PReLU(c // 2), |
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nn.ConvTranspose2d(c // 2, 4, 4, 2, 1), |
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) |
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self.conv2 = nn.Sequential( |
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nn.ConvTranspose2d(c, c // 2, 4, 2, 1), |
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nn.PReLU(c // 2), |
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nn.ConvTranspose2d(c // 2, 1, 4, 2, 1), |
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) |
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def forward(self, x, flow, scale=1): |
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x = F.interpolate( |
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x, scale_factor=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False |
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) |
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flow = ( |
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F.interpolate( |
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flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False |
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) |
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* 1.0 |
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/ scale |
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) |
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feat = self.conv0(torch.cat((x, flow), 1)) |
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feat = self.convblock0(feat) + feat |
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feat = self.convblock1(feat) + feat |
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feat = self.convblock2(feat) + feat |
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feat = self.convblock3(feat) + feat |
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flow = self.conv1(feat) |
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mask = self.conv2(feat) |
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flow = ( |
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F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) |
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* scale |
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) |
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mask = F.interpolate( |
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mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False |
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) |
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return flow, mask |
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class IFNet(nn.Module): |
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def __init__(self): |
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super(IFNet, self).__init__() |
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self.block0 = IFBlock(7 + 4, c=90) |
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self.block1 = IFBlock(7 + 4, c=90) |
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self.block2 = IFBlock(7 + 4, c=90) |
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self.block_tea = IFBlock(10 + 4, c=90) |
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def forward(self, x, scale_list=[4, 2, 1], training=False): |
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if training == False: |
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channel = x.shape[1] // 2 |
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img0 = x[:, :channel] |
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img1 = x[:, channel:] |
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flow_list = [] |
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merged = [] |
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mask_list = [] |
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warped_img0 = img0 |
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warped_img1 = img1 |
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flow = (x[:, :4]).detach() * 0 |
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mask = (x[:, :1]).detach() * 0 |
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loss_cons = 0 |
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block = [self.block0, self.block1, self.block2] |
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for i in range(3): |
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f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) |
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f1, m1 = block[i]( |
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torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), |
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torch.cat((flow[:, 2:4], flow[:, :2]), 1), |
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scale=scale_list[i], |
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) |
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flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 |
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mask = mask + (m0 + (-m1)) / 2 |
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mask_list.append(mask) |
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flow_list.append(flow) |
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warped_img0 = warp(img0, flow[:, :2]) |
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warped_img1 = warp(img1, flow[:, 2:4]) |
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merged.append((warped_img0, warped_img1)) |
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""" |
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c0 = self.contextnet(img0, flow[:, :2]) |
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c1 = self.contextnet(img1, flow[:, 2:4]) |
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tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) |
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res = tmp[:, 1:4] * 2 - 1 |
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""" |
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for i in range(3): |
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mask_list[i] = torch.sigmoid(mask_list[i]) |
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merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) |
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return flow_list, mask_list[2], merged |
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