diff --git a/RIFE_HDv2.py b/RIFE_HDv2.py new file mode 100644 index 0000000..9f19ae2 --- /dev/null +++ b/RIFE_HDv2.py @@ -0,0 +1,247 @@ +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 +from model.IFNet_HDv2 import * +import torch.nn.functional as F +from model.loss import * + +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) + ) + +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), + ) + +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 = 32 + +class ContextNet(nn.Module): + def __init__(self): + super(ContextNet, self).__init__() + self.conv0 = Conv2(3, c) + self.conv1 = Conv2(c, 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.conv0(x) + 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 FusionNet(nn.Module): + def __init__(self): + super(FusionNet, self).__init__() + self.conv0 = Conv2(10, c) + self.down0 = Conv2(c, 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.ConvTranspose2d(c, 4, 4, 2, 1) + + def forward(self, img0, img1, flow, c0, c1, flow_gt): + warped_img0 = warp(img0, flow[:, :2]) + warped_img1 = warp(img1, flow[:, 2:4]) + if flow_gt == None: + warped_img0_gt, warped_img1_gt = None, None + else: + warped_img0_gt = warp(img0, flow_gt[:, :2]) + warped_img1_gt = warp(img1, flow_gt[:, 2:4]) + x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1)) + s0 = self.down0(x) + 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 x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt + + +class Model: + def __init__(self, local_rank=-1): + self.flownet = IFNet() + self.contextnet = ContextNet() + self.fusionnet = FusionNet() + self.device() + self.optimG = AdamW(itertools.chain( + self.flownet.parameters(), + self.contextnet.parameters(), + self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5) + self.schedulerG = optim.lr_scheduler.CyclicLR( + self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False) + self.epe = EPE() + self.ter = Ternary() + self.sobel = SOBEL() + if local_rank != -1: + self.flownet = DDP(self.flownet, device_ids=[ + local_rank], output_device=local_rank) + self.contextnet = DDP(self.contextnet, device_ids=[ + local_rank], output_device=local_rank) + self.fusionnet = DDP(self.fusionnet, device_ids=[ + local_rank], output_device=local_rank) + + def train(self): + self.flownet.train() + self.contextnet.train() + self.fusionnet.train() + + def eval(self): + self.flownet.eval() + self.contextnet.eval() + self.fusionnet.eval() + + def device(self): + self.flownet.to(device) + self.contextnet.to(device) + self.fusionnet.to(device) + + def load_model(self, path, rank): + def convert(param): + if rank == -1: + return { + k.replace("module.", ""): v + for k, v in param.items() + if "module." in k + } + else: + return param + if rank <= 0: + self.flownet.load_state_dict( + convert(torch.load('{}/flownet.pkl'.format(path), map_location=device))) + self.contextnet.load_state_dict( + convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device))) + self.fusionnet.load_state_dict( + convert(torch.load('{}/unet.pkl'.format(path), map_location=device))) + + def save_model(self, path, rank): + if rank == 0: + torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path)) + torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path)) + torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path)) + + def predict(self, imgs, flow, training=True, flow_gt=None, UHD=False): + img0 = imgs[:, :3] + img1 = imgs[:, 3:] + if UHD: + flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 + c0 = self.contextnet(img0, flow[:, :2]) + c1 = self.contextnet(img1, flow[:, 2:4]) + flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", + align_corners=False) * 2.0 + refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet( + img0, img1, flow, c0, c1, flow_gt) + res = torch.sigmoid(refine_output[:, :3]) * 2 - 1 + mask = torch.sigmoid(refine_output[:, 3:4]) + merged_img = warped_img0 * mask + warped_img1 * (1 - mask) + pred = merged_img + res + pred = torch.clamp(pred, 0, 1) + if training: + return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt + else: + return pred + + def inference(self, img0, img1, UHD=False): + imgs = torch.cat((img0, img1), 1) + flow, _ = self.flownet(imgs, UHD) + return self.predict(imgs, flow, training=False, UHD=UHD) + + def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None): + for param_group in self.optimG.param_groups: + param_group['lr'] = learning_rate + if training: + self.train() + else: + self.eval() + flow, flow_list = self.flownet(imgs) + pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict( + imgs, flow, flow_gt=flow_gt) + loss_ter = self.ter(pred, gt).mean() + if training: + with torch.no_grad(): + loss_flow = torch.abs(warped_img0_gt - gt).mean() + loss_mask = torch.abs( + merged_img - gt).sum(1, True).float().detach() + loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear", + align_corners=False).detach() + flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear", + align_corners=False) * 0.5).detach() + loss_cons = 0 + for i in range(4): + loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1) + loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1) + loss_cons = loss_cons.mean() * 0.01 + else: + loss_cons = torch.tensor([0]) + loss_flow = torch.abs(warped_img0 - gt).mean() + loss_mask = 1 + loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean() + if training: + self.optimG.zero_grad() + loss_G = loss_l1 + loss_cons + loss_ter + loss_G.backward() + self.optimG.step() + return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask + + +if __name__ == '__main__': + img0 = torch.zeros(3, 3, 256, 256).float().to(device) + img1 = torch.tensor(np.random.normal( + 0, 1, (3, 3, 256, 256))).float().to(device) + imgs = torch.cat((img0, img1), 1) + model = Model() + model.eval() + print(model.inference(imgs).shape) diff --git a/model/IFNet_HDv2.py b/model/IFNet_HDv2.py new file mode 100644 index 0000000..6ed293a --- /dev/null +++ b/model/IFNet_HDv2.py @@ -0,0 +1,92 @@ +import torch +import numpy as np +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") + +def conv_wo_act(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 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) + ) + +class IFBlock(nn.Module): + def __init__(self, in_planes, scale=1, c=64): + super(IFBlock, self).__init__() + self.scale = scale + self.conv0 = conv(in_planes, c, 5, 2, 2) + self.convblock = nn.Sequential( + conv(c, c), + conv(c, c), + conv(c, c), + conv(c, c), + conv(c, c), + conv(c, c), + conv(c, c), + conv(c, c), + ) + self.conv1 = nn.Conv2d(c, 4, 3, 1, 1) + + def forward(self, x): + if self.scale != 1: + x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear", + align_corners=False) + x = self.conv0(x) + x = self.convblock(x) + x = self.conv1(x) + flow = x + if self.scale != 1: + flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear", + align_corners=False) * self.scale + return flow + + +class IFNet(nn.Module): + def __init__(self): + super(IFNet, self).__init__() + self.block0 = IFBlock(6, scale=8, c=192) + self.block1 = IFBlock(10, scale=4, c=128) + self.block2 = IFBlock(10, scale=2, c=96) + self.block3 = IFBlock(10, scale=1, c=48) + + def forward(self, x, UHD=False): + if UHD: + x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) + flow0 = self.block0(x) + F1 = flow0 + F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0 + warped_img0 = warp(x[:, :3], F1_large[:, :2]) + warped_img1 = warp(x[:, 3:], F1_large[:, 2:4]) + flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1)) + F2 = (flow0 + flow1) + F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0 + warped_img0 = warp(x[:, :3], F2_large[:, :2]) + warped_img1 = warp(x[:, 3:], F2_large[:, 2:4]) + flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1)) + F3 = (flow0 + flow1 + flow2) + F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0 + warped_img0 = warp(x[:, :3], F3_large[:, :2]) + warped_img1 = warp(x[:, 3:], F3_large[:, 2:4]) + flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1)) + F4 = (flow0 + flow1 + flow2 + flow3) + return F4, [F1, F2, F3, F4] + +if __name__ == '__main__': + img0 = torch.zeros(3, 3, 256, 256).float().to(device) + img1 = torch.tensor(np.random.normal( + 0, 1, (3, 3, 256, 256))).float().to(device) + imgs = torch.cat((img0, img1), 1) + flownet = IFNet() + flow, _ = flownet(imgs) + print(flow.shape)