import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import kiui from kiui.lpips import LPIPS from core.unet import UNet from core.options import Options from core.gs import GaussianRenderer class LGM(nn.Module): def __init__( self, opt: Options, ): super().__init__() self.opt = opt # unet self.unet = UNet( 9, 14, down_channels=self.opt.down_channels, down_attention=self.opt.down_attention, mid_attention=self.opt.mid_attention, up_channels=self.opt.up_channels, up_attention=self.opt.up_attention, ) # last conv self.conv = nn.Conv2d(14, 14, kernel_size=1) # NOTE: maybe remove it if train again # Gaussian Renderer self.gs = GaussianRenderer(opt) # activations... self.pos_act = lambda x: x.clamp(-1, 1) self.scale_act = lambda x: 0.1 * F.softplus(x) self.opacity_act = lambda x: torch.sigmoid(x) self.rot_act = F.normalize self.rgb_act = lambda x: 0.5 * torch.tanh(x) + 0.5 # NOTE: may use sigmoid if train again # LPIPS loss if self.opt.lambda_lpips > 0: self.lpips_loss = LPIPS(net='vgg') self.lpips_loss.requires_grad_(False) def state_dict(self, **kwargs): # remove lpips_loss state_dict = super().state_dict(**kwargs) for k in list(state_dict.keys()): if 'lpips_loss' in k: del state_dict[k] return state_dict def prepare_default_rays(self, device, elevation=0): from kiui.cam import orbit_camera from core.utils import get_rays cam_poses = np.stack([ orbit_camera(elevation, 0, radius=self.opt.cam_radius), orbit_camera(elevation, 90, radius=self.opt.cam_radius), orbit_camera(elevation, 180, radius=self.opt.cam_radius), orbit_camera(elevation, 270, radius=self.opt.cam_radius), ], axis=0) # [4, 4, 4] cam_poses = torch.from_numpy(cam_poses) rays_embeddings = [] for i in range(cam_poses.shape[0]): rays_o, rays_d = get_rays(cam_poses[i], self.opt.input_size, self.opt.input_size, self.opt.fovy) # [h, w, 3] rays_plucker = torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d], dim=-1) # [h, w, 6] rays_embeddings.append(rays_plucker) ## visualize rays for plotting figure # kiui.vis.plot_image(rays_d * 0.5 + 0.5, save=True) rays_embeddings = torch.stack(rays_embeddings, dim=0).permute(0, 3, 1, 2).contiguous().to(device) # [V, 6, h, w] return rays_embeddings def forward_gaussians(self, images): # images: [B, 4, 9, H, W] # return: Gaussians: [B, dim_t] B, V, C, H, W = images.shape images = images.view(B*V, C, H, W) x = self.unet(images) # [B*4, 14, h, w] x = self.conv(x) # [B*4, 14, h, w] x = x.reshape(B, 4, 14, self.opt.splat_size, self.opt.splat_size) ## visualize multi-view gaussian features for plotting figure # tmp_alpha = self.opacity_act(x[0, :, 3:4]) # tmp_img_rgb = self.rgb_act(x[0, :, 11:]) * tmp_alpha + (1 - tmp_alpha) # tmp_img_pos = self.pos_act(x[0, :, 0:3]) * 0.5 + 0.5 # kiui.vis.plot_image(tmp_img_rgb, save=True) # kiui.vis.plot_image(tmp_img_pos, save=True) x = x.permute(0, 1, 3, 4, 2).reshape(B, -1, 14) pos = self.pos_act(x[..., 0:3]) # [B, N, 3] opacity = self.opacity_act(x[..., 3:4]) scale = self.scale_act(x[..., 4:7]) rotation = self.rot_act(x[..., 7:11]) rgbs = self.rgb_act(x[..., 11:]) rot_matrix = torch.tensor([[1.0, 0.0, 0.0, 0.0], [0.0, -1.0, 0.0, 0.0], [0.0, 0.0, -1.0, 0.0], [0.0, 0.0, 0.0, 1.0]], dtype=torch.float32, device=images.device) pos_4d = torch.cat([pos, torch.ones_like(pos[..., :1])], dim=-1) pos = torch.matmul(pos_4d, rot_matrix) # [B, N, 4] pos = pos[..., :3] rotation = torch.matmul(rotation, rot_matrix) gaussians = torch.cat([pos, opacity, scale, rotation, rgbs], dim=-1) # [B, N, 14] return gaussians def forward(self, data, step_ratio=1): # data: output of the dataloader # return: loss results = {} loss = 0 images = data['input'] # [B, 4, 9, h, W], input features # use the first view to predict gaussians gaussians = self.forward_gaussians(images) # [B, N, 14] results['gaussians'] = gaussians # random bg for training if self.training: bg_color = torch.rand(3, dtype=torch.float32, device=gaussians.device) else: bg_color = torch.ones(3, dtype=torch.float32, device=gaussians.device) # use the other views for rendering and supervision results = self.gs.render(gaussians, data['cam_view'], data['cam_view_proj'], data['cam_pos'], bg_color=bg_color) pred_images = results['image'] # [B, V, C, output_size, output_size] pred_alphas = results['alpha'] # [B, V, 1, output_size, output_size] results['images_pred'] = pred_images results['alphas_pred'] = pred_alphas gt_images = data['images_output'] # [B, V, 3, output_size, output_size], ground-truth novel views gt_masks = data['masks_output'] # [B, V, 1, output_size, output_size], ground-truth masks gt_images = gt_images * gt_masks + bg_color.view(1, 1, 3, 1, 1) * (1 - gt_masks) loss_mse = F.mse_loss(pred_images, gt_images) + F.mse_loss(pred_alphas, gt_masks) loss = loss + loss_mse if self.opt.lambda_lpips > 0: loss_lpips = self.lpips_loss( # gt_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, # pred_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, # downsampled to at most 256 to reduce memory cost F.interpolate(gt_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, (256, 256), mode='bilinear', align_corners=False), F.interpolate(pred_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, (256, 256), mode='bilinear', align_corners=False), ).mean() results['loss_lpips'] = loss_lpips loss = loss + self.opt.lambda_lpips * loss_lpips results['loss'] = loss # metric with torch.no_grad(): psnr = -10 * torch.log10(torch.mean((pred_images.detach() - gt_images) ** 2)) results['psnr'] = psnr return results