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_v3_legacy.warplayer import warp from torch.nn.parallel import DistributedDataParallel as DDP from model_v3_legacy.IFNet_HDv3 import * import torch.nn.functional as F from model_v3_legacy.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) scale_list = [8, 4, 2] flow, _ = self.flownet(imgs, scale_list) res = self.predict(imgs, flow, training=False, UHD=False) return res 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)