import torch import torch.nn as nn import numpy as np import torch.optim as optim import itertools from model.warplayer import warp import torch.nn.functional as F 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 deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1): return nn.Sequential( torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True), nn.PReLU(out_planes) ) class Conv2(nn.Module): def __init__(self, in_planes, out_planes, stride=2): super(Conv2, self).__init__() self.conv1 = conv(in_planes, out_planes, 3, stride, 1) self.conv2 = conv(out_planes, out_planes, 3, 1, 1) def forward(self, x): x = self.conv1(x) x = self.conv2(x) return x c = 16 class Contextnet(nn.Module): def __init__(self): super(Contextnet, self).__init__() self.conv1 = Conv2(3, c) self.conv2 = Conv2(c, 2*c) self.conv3 = Conv2(2*c, 4*c) self.conv4 = Conv2(4*c, 8*c) def forward(self, x, flow): x = self.conv1(x) flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5 f1 = warp(x, flow) x = self.conv2(x) flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5 f2 = warp(x, flow) x = self.conv3(x) flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5 f3 = warp(x, flow) x = self.conv4(x) flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5 f4 = warp(x, flow) return [f1, f2, f3, f4] class Unet(nn.Module): def __init__(self): super(Unet, self).__init__() self.down0 = Conv2(17, 2*c) self.down1 = Conv2(4*c, 4*c) self.down2 = Conv2(8*c, 8*c) self.down3 = Conv2(16*c, 16*c) self.up0 = deconv(32*c, 8*c) self.up1 = deconv(16*c, 4*c) self.up2 = deconv(8*c, 2*c) self.up3 = deconv(4*c, c) self.conv = nn.Conv2d(c, 3, 3, 1, 1) def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1): s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1)) s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1)) s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1)) s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1)) x = self.up0(torch.cat((s3, c0[3], c1[3]), 1)) x = self.up1(torch.cat((x, s2), 1)) x = self.up2(torch.cat((x, s1), 1)) x = self.up3(torch.cat((x, s0), 1)) x = self.conv(x) return torch.sigmoid(x)