| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from torch.optim import AdamW | |
| import torch.optim as optim | |
| import itertools | |
| from model.warplayer import warp | |
| from torch.nn.parallel import DistributedDataParallel as DDP | |
| import torch.nn.functional as F | |
| device = torch.device("cuda") | |
| 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.LeakyReLU(0.2, True) | |
| ) | |
| def conv_woact(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 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.LeakyReLU(0.2, True) | |
| ) | |
| 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) * 0.5 | |
| f1 = warp(x, flow) | |
| x = self.conv2(x) | |
| flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 | |
| f2 = warp(x, flow) | |
| x = self.conv3(x) | |
| flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 | |
| f3 = warp(x, flow) | |
| x = self.conv4(x) | |
| flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=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) | |