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| 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.oldmodel.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-4) | |
| 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): | |
| img0 = imgs[:, :3] | |
| img1 = imgs[:, 3:] | |
| 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, scale=1.0): | |
| imgs = torch.cat((img0, img1), 1) | |
| flow, _ = self.flownet(imgs, scale) | |
| return self.predict(imgs, flow, training=False) | |
| 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) | |