import os import time import numpy as np import torch import torch.nn.functional as F import gc from torchvision.transforms import v2 from torchvision.utils import make_grid, save_image from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity import pytorch_lightning as pl from einops import rearrange, repeat from src.utils.camera_util import FOV_to_intrinsics from src.utils.material import Material from src.utils.train_util import instantiate_from_config import nvdiffrast.torch as dr from src.utils import render from src.utils.mesh import Mesh, compute_tangents os.environ['PYOPENGL_PLATFORM'] = 'egl' # from pytorch3d.transforms import quaternion_to_matrix, euler_angles_to_matrix GLCTX = [None] * torch.cuda.device_count() def initialize_extension(gpu_id): global GLCTX if GLCTX[gpu_id] is None: print(f"Initializing extension module renderutils_plugin on GPU {gpu_id}...") torch.cuda.set_device(gpu_id) GLCTX[gpu_id] = dr.RasterizeCudaContext() return GLCTX[gpu_id] # Regulrarization loss for FlexiCubes def sdf_reg_loss_batch(sdf, all_edges): sdf_f1x6x2 = sdf[:, all_edges.reshape(-1)].reshape(sdf.shape[0], -1, 2) mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1]) sdf_f1x6x2 = sdf_f1x6x2[mask] sdf_diff = F.binary_cross_entropy_with_logits( sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()) + \ F.binary_cross_entropy_with_logits( sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float()) return sdf_diff def rotate_x(a, device=None): s, c = np.sin(a), np.cos(a) return torch.tensor([[1, 0, 0, 0], [0, c,-s, 0], [0, s, c, 0], [0, 0, 0, 1]], dtype=torch.float32, device=device) def convert_to_white_bg(image, write_bg=True): alpha = image[:, :, 3:] if write_bg: return image[:, :, :3] * alpha + 1. * (1 - alpha) else: return image[:, :, :3] * alpha class MVRecon(pl.LightningModule): def __init__( self, lrm_generator_config, input_size=256, render_size=512, init_ckpt=None, use_tv_loss=True, mesh_save_root="Objaverse_highQuality", sample_points=None, use_gt_albedo=False, ): super(MVRecon, self).__init__() self.use_gt_albedo = use_gt_albedo self.use_tv_loss = use_tv_loss self.input_size = input_size self.render_size = render_size self.mesh_save_root = mesh_save_root self.sample_points = sample_points self.lrm_generator = instantiate_from_config(lrm_generator_config) self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg') if init_ckpt is not None: sd = torch.load(init_ckpt, map_location='cpu')['state_dict'] sd = {k: v for k, v in sd.items() if k.startswith('lrm_generator')} sd_fc = {} for k, v in sd.items(): if k.startswith('lrm_generator.synthesizer.decoder.net.'): if k.startswith('lrm_generator.synthesizer.decoder.net.6.'): # last layer # Here we assume the density filed's isosurface threshold is t, # we reverse the sign of density filed to initialize SDF field. # -(w*x + b - t) = (-w)*x + (t - b) if 'weight' in k: sd_fc[k.replace('net.', 'net_sdf.')] = -v[0:1] else: sd_fc[k.replace('net.', 'net_sdf.')] = 10.0 - v[0:1] sd_fc[k.replace('net.', 'net_rgb.')] = v[1:4] else: sd_fc[k.replace('net.', 'net_sdf.')] = v sd_fc[k.replace('net.', 'net_rgb.')] = v else: sd_fc[k] = v sd_fc = {k.replace('lrm_generator.', ''): v for k, v in sd_fc.items()} # missing `net_deformation` and `net_weight` parameters self.lrm_generator.load_state_dict(sd_fc, strict=False) print(f'Loaded weights from {init_ckpt}') self.validation_step_outputs = [] def on_fit_start(self): device = torch.device(f'cuda:{self.local_rank}') self.lrm_generator.init_flexicubes_geometry(device) if self.global_rank == 0: os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True) os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True) def collate_fn(self, batch): gpu_id = torch.cuda.current_device() # 获取当前线程的 GPU ID glctx = initialize_extension(gpu_id) batch_size = len(batch) input_view_num = batch[0]["input_view_num"] target_view_num = batch[0]["target_view_num"] iter_res = [512, 512] iter_spp = 1 layers = 1 # Initialize lists for input and target data input_images, input_alphas, input_depths, input_normals, input_albedos = [], [], [], [], [] input_spec_light, input_diff_light, input_spec_albedo,input_diff_albedo = [], [], [], [] input_w2cs, input_Ks, input_camera_pos, input_c2ws = [], [], [], [] input_env, input_materials = [], [] input_camera_embeddings = [] # camera_embedding_list target_images, target_alphas, target_depths, target_normals, target_albedos = [], [], [], [], [] target_spec_light, target_diff_light, target_spec_albedo, target_diff_albedo = [], [], [], [] target_w2cs, target_Ks, target_camera_pos = [], [], [] target_env, target_materials = [], [] for sample in batch: obj_path = sample['obj_path'] with torch.no_grad(): mesh_attributes = sample['mesh_attributes'] v_pos = mesh_attributes["v_pos"].to(self.device) v_nrm = mesh_attributes["v_nrm"].to(self.device) v_tex = mesh_attributes["v_tex"].to(self.device) v_tng = mesh_attributes["v_tng"].to(self.device) t_pos_idx = mesh_attributes["t_pos_idx"].to(self.device) t_nrm_idx = mesh_attributes["t_nrm_idx"].to(self.device) t_tex_idx = mesh_attributes["t_tex_idx"].to(self.device) t_tng_idx = mesh_attributes["t_tng_idx"].to(self.device) material = Material(mesh_attributes["mat_dict"]) material = material.to(self.device) ref_mesh = Mesh(v_pos=v_pos, v_nrm=v_nrm, v_tex=v_tex, v_tng=v_tng, t_pos_idx=t_pos_idx, t_nrm_idx=t_nrm_idx, t_tex_idx=t_tex_idx, t_tng_idx=t_tng_idx, material=material) pose_list_sample = sample['pose_list'] # mvp camera_pos_sample = sample['camera_pos'] # campos, mv.inverse c2w_list_sample = sample['c2w_list'] # mv env_list_sample = sample['env_list'] material_list_sample = sample['material_list'] camera_embeddings = sample["camera_embedding_list"] fov_deg = sample['fov_deg'] raduis = sample['raduis'] # print(f"fov_deg:{fov_deg}, raduis:{raduis}") sample_input_images, sample_input_alphas, sample_input_depths, sample_input_normals, sample_input_albedos = [], [], [], [], [] sample_input_w2cs, sample_input_Ks, sample_input_camera_pos, sample_input_c2ws = [], [], [], [] sample_input_camera_embeddings = [] sample_input_spec_light, sample_input_diff_light = [], [] sample_target_images, sample_target_alphas, sample_target_depths, sample_target_normals, sample_target_albedos = [], [], [], [], [] sample_target_w2cs, sample_target_Ks, sample_target_camera_pos = [], [], [] sample_target_spec_light, sample_target_diff_light = [], [] sample_input_env = [] sample_input_materials = [] sample_target_env = [] sample_target_materials = [] for i in range(len(pose_list_sample)): mvp = pose_list_sample[i] campos = camera_pos_sample[i] env = env_list_sample[i] materials = material_list_sample[i] camera_embedding = camera_embeddings[i] with torch.no_grad(): buffer_dict = render.render_mesh(glctx, ref_mesh, mvp.to(self.device), campos.to(self.device), [env], None, None, materials, iter_res, spp=iter_spp, num_layers=layers, msaa=True, background=None, gt_render=True) image = convert_to_white_bg(buffer_dict['shaded'][0]) albedo = convert_to_white_bg(buffer_dict['albedo'][0]).clamp(0., 1.) alpha = buffer_dict['mask'][0][:, :, 3:] depth = convert_to_white_bg(buffer_dict['depth'][0]) normal = convert_to_white_bg(buffer_dict['gb_normal'][0], write_bg=False) spec_light = convert_to_white_bg(buffer_dict['spec_light'][0]) diff_light = convert_to_white_bg(buffer_dict['diff_light'][0]) if i < input_view_num: sample_input_images.append(image) sample_input_albedos.append(albedo) sample_input_alphas.append(alpha) sample_input_depths.append(depth) sample_input_normals.append(normal) sample_input_spec_light.append(spec_light) sample_input_diff_light.append(diff_light) sample_input_w2cs.append(mvp) sample_input_camera_pos.append(campos) sample_input_c2ws.append(c2w_list_sample[i]) sample_input_Ks.append(FOV_to_intrinsics(fov_deg)) sample_input_env.append(env) sample_input_materials.append(materials) sample_input_camera_embeddings.append(camera_embedding) else: sample_target_images.append(image) sample_target_albedos.append(albedo) sample_target_alphas.append(alpha) sample_target_depths.append(depth) sample_target_normals.append(normal) sample_target_spec_light.append(spec_light) sample_target_diff_light.append(diff_light) sample_target_w2cs.append(mvp) sample_target_camera_pos.append(campos) sample_target_Ks.append(FOV_to_intrinsics(fov_deg)) sample_target_env.append(env) sample_target_materials.append(materials) input_images.append(torch.stack(sample_input_images, dim=0).permute(0, 3, 1, 2)) input_albedos.append(torch.stack(sample_input_albedos, dim=0).permute(0, 3, 1, 2)) input_alphas.append(torch.stack(sample_input_alphas, dim=0).permute(0, 3, 1, 2)) input_depths.append(torch.stack(sample_input_depths, dim=0).permute(0, 3, 1, 2)) input_normals.append(torch.stack(sample_input_normals, dim=0).permute(0, 3, 1, 2)) input_spec_light.append(torch.stack(sample_input_spec_light, dim=0).permute(0, 3, 1, 2)) input_diff_light.append(torch.stack(sample_input_diff_light, dim=0).permute(0, 3, 1, 2)) input_w2cs.append(torch.stack(sample_input_w2cs, dim=0)) input_camera_pos.append(torch.stack(sample_input_camera_pos, dim=0)) input_c2ws.append(torch.stack(sample_input_c2ws, dim=0)) input_camera_embeddings.append(torch.stack(sample_input_camera_embeddings, dim=0)) input_Ks.append(torch.stack(sample_input_Ks, dim=0)) input_env.append(sample_input_env) input_materials.append(sample_input_materials) target_images.append(torch.stack(sample_target_images, dim=0).permute(0, 3, 1, 2)) target_albedos.append(torch.stack(sample_target_albedos, dim=0).permute(0, 3, 1, 2)) target_alphas.append(torch.stack(sample_target_alphas, dim=0).permute(0, 3, 1, 2)) target_depths.append(torch.stack(sample_target_depths, dim=0).permute(0, 3, 1, 2)) target_normals.append(torch.stack(sample_target_normals, dim=0).permute(0, 3, 1, 2)) target_spec_light.append(torch.stack(sample_target_spec_light, dim=0).permute(0, 3, 1, 2)) target_diff_light.append(torch.stack(sample_target_diff_light, dim=0).permute(0, 3, 1, 2)) target_w2cs.append(torch.stack(sample_target_w2cs, dim=0)) target_camera_pos.append(torch.stack(sample_target_camera_pos, dim=0)) target_Ks.append(torch.stack(sample_target_Ks, dim=0)) target_env.append(sample_target_env) target_materials.append(sample_target_materials) del ref_mesh del material del mesh_attributes torch.cuda.empty_cache() gc.collect() data = { 'input_images': torch.stack(input_images, dim=0).detach().cpu(), # (batch_size, input_view_num, 3, H, W) 'input_alphas': torch.stack(input_alphas, dim=0).detach().cpu(), # (batch_size, input_view_num, 1, H, W) 'input_depths': torch.stack(input_depths, dim=0).detach().cpu(), 'input_normals': torch.stack(input_normals, dim=0).detach().cpu(), 'input_albedos': torch.stack(input_albedos, dim=0).detach().cpu(), 'input_spec_light': torch.stack(input_spec_light, dim=0).detach().cpu(), 'input_diff_light': torch.stack(input_diff_light, dim=0).detach().cpu(), 'input_materials': input_materials, 'input_w2cs': torch.stack(input_w2cs, dim=0).squeeze(2), # (batch_size, input_view_num, 4, 4) 'input_Ks': torch.stack(input_Ks, dim=0).float(), # (batch_size, input_view_num, 3, 3) 'input_env': input_env, 'input_camera_pos': torch.stack(input_camera_pos, dim=0).squeeze(2), # (batch_size, input_view_num, 3) 'input_c2ws': torch.stack(input_c2ws, dim=0).squeeze(2), # (batch_size, input_view_num, 4, 4) 'input_camera_embedding': torch.stack(input_camera_embeddings, dim=0).squeeze(2), 'target_sample_points': None, 'target_images': torch.stack(target_images, dim=0).detach().cpu(), # (batch_size, target_view_num, 3, H, W) 'target_alphas': torch.stack(target_alphas, dim=0).detach().cpu(), # (batch_size, target_view_num, 1, H, W) 'target_depths': torch.stack(target_depths, dim=0).detach().cpu(), 'target_normals': torch.stack(target_normals, dim=0).detach().cpu(), 'target_albedos': torch.stack(target_albedos, dim=0).detach().cpu(), 'target_spec_light': torch.stack(target_spec_light, dim=0).detach().cpu(), 'target_diff_light': torch.stack(target_diff_light, dim=0).detach().cpu(), 'target_materials': target_materials, 'target_w2cs': torch.stack(target_w2cs, dim=0).squeeze(2), # (batch_size, target_view_num, 4, 4) 'target_Ks': torch.stack(target_Ks, dim=0).float(), # (batch_size, target_view_num, 3, 3) 'target_env': target_env, 'target_camera_pos': torch.stack(target_camera_pos, dim=0).squeeze(2) # (batch_size, target_view_num, 3) } return data def prepare_batch_data(self, batch): # breakpoint() lrm_generator_input = {} render_gt = {} # input images images = batch['input_images'] images = v2.functional.resize(images, self.input_size, interpolation=3, antialias=True).clamp(0, 1) batch_size = images.shape[0] # breakpoint() lrm_generator_input['images'] = images.to(self.device) # input cameras and render cameras # input_c2ws = batch['input_c2ws'] input_Ks = batch['input_Ks'] # target_c2ws = batch['target_c2ws'] input_camera_embedding = batch["input_camera_embedding"].to(self.device) input_w2cs = batch['input_w2cs'] target_w2cs = batch['target_w2cs'] render_w2cs = torch.cat([input_w2cs, target_w2cs], dim=1) input_camera_pos = batch['input_camera_pos'] target_camera_pos = batch['target_camera_pos'] render_camera_pos = torch.cat([input_camera_pos, target_camera_pos], dim=1) input_extrinsics = input_camera_embedding.flatten(-2) input_extrinsics = input_extrinsics[:, :, :12] input_intrinsics = input_Ks.flatten(-2).to(self.device) input_intrinsics = torch.stack([ input_intrinsics[:, :, 0], input_intrinsics[:, :, 4], input_intrinsics[:, :, 2], input_intrinsics[:, :, 5], ], dim=-1) cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1) # add noise to input_cameras cameras = cameras + torch.rand_like(cameras) * 0.04 - 0.02 lrm_generator_input['cameras'] = cameras.to(self.device) lrm_generator_input['render_cameras'] = render_w2cs.to(self.device) lrm_generator_input['cameras_pos'] = render_camera_pos.to(self.device) lrm_generator_input['env'] = [] lrm_generator_input['materials'] = [] for i in range(batch_size): lrm_generator_input['env'].append( batch['input_env'][i] + batch['target_env'][i]) lrm_generator_input['materials'].append( batch['input_materials'][i] + batch['target_materials'][i]) lrm_generator_input['albedo'] = torch.cat([batch['input_albedos'],batch['target_albedos']],dim=1) # target images target_images = torch.cat([batch['input_images'], batch['target_images']], dim=1) target_albedos = torch.cat([batch['input_albedos'], batch['target_albedos']], dim=1) target_depths = torch.cat([batch['input_depths'], batch['target_depths']], dim=1) target_alphas = torch.cat([batch['input_alphas'], batch['target_alphas']], dim=1) target_normals = torch.cat([batch['input_normals'], batch['target_normals']], dim=1) target_spec_lights = torch.cat([batch['input_spec_light'], batch['target_spec_light']], dim=1) target_diff_lights = torch.cat([batch['input_diff_light'], batch['target_diff_light']], dim=1) render_size = self.render_size target_images = v2.functional.resize( target_images, render_size, interpolation=3, antialias=True).clamp(0, 1) target_depths = v2.functional.resize( target_depths, render_size, interpolation=0, antialias=True) target_alphas = v2.functional.resize( target_alphas, render_size, interpolation=0, antialias=True) target_normals = v2.functional.resize( target_normals, render_size, interpolation=3, antialias=True) lrm_generator_input['render_size'] = render_size render_gt['target_sample_points'] = batch['target_sample_points'] render_gt['target_images'] = target_images.to(self.device) render_gt['target_albedos'] = target_albedos.to(self.device) render_gt['target_depths'] = target_depths.to(self.device) render_gt['target_alphas'] = target_alphas.to(self.device) render_gt['target_normals'] = target_normals.to(self.device) render_gt['target_spec_lights'] = target_spec_lights.to(self.device) render_gt['target_diff_lights'] = target_diff_lights.to(self.device) # render_gt['target_spec_albedos'] = target_spec_albedos.to(self.device) # render_gt['target_diff_albedos'] = target_diff_albedos.to(self.device) return lrm_generator_input, render_gt def prepare_validation_batch_data(self, batch): lrm_generator_input = {} # input images images = batch['input_images'] images = v2.functional.resize( images, self.input_size, interpolation=3, antialias=True).clamp(0, 1) lrm_generator_input['images'] = images.to(self.device) lrm_generator_input['specular_light'] = batch['specular'] lrm_generator_input['diffuse_light'] = batch['diffuse'] lrm_generator_input['metallic'] = batch['input_metallics'] lrm_generator_input['roughness'] = batch['input_roughness'] proj = self.perspective(0.449, 1, 0.1, 1000., self.device) # input cameras input_c2ws = batch['input_c2ws'].flatten(-2) input_Ks = batch['input_Ks'].flatten(-2) input_extrinsics = input_c2ws[:, :, :12] input_intrinsics = torch.stack([ input_Ks[:, :, 0], input_Ks[:, :, 4], input_Ks[:, :, 2], input_Ks[:, :, 5], ], dim=-1) cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1) lrm_generator_input['cameras'] = cameras.to(self.device) # render cameras render_c2ws = batch['render_c2ws'] lrm_generator_input['camera_pos'] = torch.linalg.inv(render_w2cs.to(self.device) @ rotate_x(np.pi / 2, self.device))[..., :3, 3] render_w2cs = ( render_w2cs @ rotate_x(np.pi / 2) ) lrm_generator_input['render_cameras'] = render_w2cs.to(self.device) lrm_generator_input['render_size'] = 384 return lrm_generator_input def forward_lrm_generator(self, images, cameras, camera_pos,env, materials, albedo_map, render_cameras, render_size=512, sample_points=None, gt_albedo_map=None): planes = torch.utils.checkpoint.checkpoint( self.lrm_generator.forward_planes, images, cameras, use_reentrant=False, ) out = self.lrm_generator.forward_geometry( planes, render_cameras, camera_pos, env, materials, albedo_map, render_size, sample_points, gt_albedo_map ) return out def forward(self, lrm_generator_input, gt_albedo_map=None): images = lrm_generator_input['images'] cameras = lrm_generator_input['cameras'] render_cameras = lrm_generator_input['render_cameras'] render_size = lrm_generator_input['render_size'] env = lrm_generator_input['env'] materials = lrm_generator_input['materials'] albedo_map = lrm_generator_input['albedo'] camera_pos = lrm_generator_input['cameras_pos'] out = self.forward_lrm_generator( images, cameras, camera_pos, env, materials, albedo_map, render_cameras, render_size=render_size, sample_points=self.sample_points, gt_albedo_map=gt_albedo_map) return out def training_step(self, batch, batch_idx): batch = self.collate_fn(batch) lrm_generator_input, render_gt = self.prepare_batch_data(batch) if self.use_gt_albedo: gt_albedo_map = render_gt['target_albedos'] else: gt_albedo_map = None render_out = self.forward(lrm_generator_input, gt_albedo_map=gt_albedo_map) loss, loss_dict = self.compute_loss(render_out, render_gt) self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True, batch_size=len(batch['input_images']), sync_dist=True) if self.global_step % 20 == 0 and self.global_rank == 0 : B, N, C, H, W = render_gt['target_images'].shape N_in = lrm_generator_input['images'].shape[1] target_images = rearrange(render_gt['target_images'], 'b n c h w -> b c h (n w)') render_images = rearrange(render_out['pbr_img'], 'b n c h w -> b c h (n w)') target_alphas = rearrange(repeat(render_gt['target_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') target_spec_light = rearrange(render_gt['target_spec_lights'], 'b n c h w -> b c h (n w)') target_diff_light = rearrange(render_gt['target_diff_lights'], 'b n c h w -> b c h (n w)') render_alphas = rearrange(render_out['mask'], 'b n c h w -> b c h (n w)') render_albodos = rearrange(render_out['albedo'], 'b n c h w -> b c h (n w)') target_albedos = rearrange(render_gt['target_albedos'], 'b n c h w -> b c h (n w)') render_spec_light = rearrange(render_out['pbr_spec_light'], 'b n c h w -> b c h (n w)') render_diffuse_light = rearrange(render_out['pbr_diffuse_light'], 'b n c h w -> b c h (n w)') render_normal = rearrange(render_out['normal_img'], 'b n c h w -> b c h (n w)') target_depths = rearrange(render_gt['target_depths'], 'b n c h w -> b c h (n w)') render_depths = rearrange(render_out['depth'], 'b n c h w -> b c h (n w)') target_normals = rearrange(render_gt['target_normals'], 'b n c h w -> b c h (n w)') MAX_DEPTH = torch.max(target_depths) target_depths = target_depths / MAX_DEPTH * target_alphas render_depths = render_depths / MAX_DEPTH * render_alphas grid = torch.cat([ target_images, render_images, target_alphas, render_alphas, target_albedos, render_albodos, target_spec_light, render_spec_light, target_diff_light, render_diffuse_light, (target_normals+1)/2, (render_normal+1)/2, target_depths, render_depths ], dim=-2).detach().cpu() grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1)) image_path = os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png') save_image(grid, image_path) print(f"Saved image to {image_path}") return loss def total_variation_loss(self, img, beta=2.0): bs_img, n_view, c_img, h_img, w_img = img.size() tv_h = torch.pow(img[...,1:,:]-img[...,:-1,:], beta).sum() tv_w = torch.pow(img[...,:,1:]-img[...,:,:-1], beta).sum() return (tv_h+tv_w)/(bs_img*n_view*c_img*h_img*w_img) def compute_loss(self, render_out, render_gt): # NOTE: the rgb value range of OpenLRM is [0, 1] render_albedo_image = render_out['albedo'] render_pbr_image = render_out['pbr_img'] render_spec_light = render_out['pbr_spec_light'] render_diff_light = render_out['pbr_diffuse_light'] target_images = render_gt['target_images'].to(render_albedo_image) target_albedos = render_gt['target_albedos'].to(render_albedo_image) target_spec_light = render_gt['target_spec_lights'].to(render_albedo_image) target_diff_light = render_gt['target_diff_lights'].to(render_albedo_image) render_images = rearrange(render_pbr_image, 'b n ... -> (b n) ...') * 2.0 - 1.0 target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0 render_albedos = rearrange(render_albedo_image, 'b n ... -> (b n) ...') * 2.0 - 1.0 target_albedos = rearrange(target_albedos, 'b n ... -> (b n) ...') * 2.0 - 1.0 render_spec_light = rearrange(render_spec_light, 'b n ... -> (b n) ...') * 2.0 - 1.0 target_spec_light = rearrange(target_spec_light, 'b n ... -> (b n) ...') * 2.0 - 1.0 render_diff_light = rearrange(render_diff_light, 'b n ... -> (b n) ...') * 2.0 - 1.0 target_diff_light = rearrange(target_diff_light, 'b n ... -> (b n) ...') * 2.0 - 1.0 loss_mse = F.mse_loss(render_images, target_images) loss_mse_albedo = F.mse_loss(render_albedos, target_albedos) loss_rgb_lpips = 2.0 * self.lpips(render_images, target_images) loss_albedo_lpips = 2.0 * self.lpips(render_albedos, target_albedos) loss_spec_light = F.mse_loss(render_spec_light, target_spec_light) loss_diff_light = F.mse_loss(render_diff_light, target_diff_light) loss_spec_light_lpips = 2.0 * self.lpips(render_spec_light.clamp(-1., 1.), target_spec_light.clamp(-1., 1.)) loss_diff_light_lpips = 2.0 * self.lpips(render_diff_light.clamp(-1., 1.), target_diff_light.clamp(-1., 1.)) render_alphas = render_out['mask'][:,:,:1,:,:] target_alphas = render_gt['target_alphas'] loss_mask = F.mse_loss(render_alphas, target_alphas) render_depths = torch.mean(render_out['depth'], dim=2, keepdim=True) target_depths = torch.mean(render_gt['target_depths'], dim=2, keepdim=True) loss_depth = 0.5 * F.l1_loss(render_depths[(target_alphas>0)], target_depths[target_alphas>0]) render_normals = render_out['normal'][...,:3].permute(0,3,1,2).unsqueeze(0) target_normals = render_gt['target_normals'] similarity = (render_normals * target_normals).sum(dim=-3).abs() normal_mask = target_alphas.squeeze(-3) loss_normal = 1 - similarity[normal_mask>0].mean() loss_normal = 0.2 * loss_normal * 1.0 # tv loss if self.use_tv_loss: triplane = render_out['triplane'] tv_loss = self.total_variation_loss(triplane, beta=2.0) # flexicubes regularization loss sdf = render_out['sdf'] sdf_reg_loss = render_out['sdf_reg_loss'] sdf_reg_loss_entropy = sdf_reg_loss_batch(sdf, self.lrm_generator.geometry.all_edges).mean() * 0.01 _, flexicubes_surface_reg, flexicubes_weights_reg = sdf_reg_loss flexicubes_surface_reg = flexicubes_surface_reg.mean() * 0.5 flexicubes_weights_reg = flexicubes_weights_reg.mean() * 0.1 loss_reg = sdf_reg_loss_entropy + flexicubes_surface_reg + flexicubes_weights_reg loss_reg = loss_reg loss = loss_mse + loss_rgb_lpips + loss_albedo_lpips + loss_mask + loss_reg + loss_mse_albedo + loss_depth + \ loss_normal + loss_spec_light + loss_diff_light + loss_spec_light_lpips + loss_diff_light_lpips if self.use_tv_loss: loss += tv_loss * 2e-4 prefix = 'train' loss_dict = {} loss_dict.update({f'{prefix}/loss_mse': loss_mse.item()}) loss_dict.update({f'{prefix}/loss_mse_albedo': loss_mse_albedo.item()}) loss_dict.update({f'{prefix}/loss_rgb_lpips': loss_rgb_lpips.item()}) loss_dict.update({f'{prefix}/loss_albedo_lpips': loss_albedo_lpips.item()}) loss_dict.update({f'{prefix}/loss_mask': loss_mask.item()}) loss_dict.update({f'{prefix}/loss_normal': loss_normal.item()}) loss_dict.update({f'{prefix}/loss_depth': loss_depth.item()}) loss_dict.update({f'{prefix}/loss_spec_light': loss_spec_light.item()}) loss_dict.update({f'{prefix}/loss_diff_light': loss_diff_light.item()}) loss_dict.update({f'{prefix}/loss_spec_light_lpips': loss_spec_light_lpips.item()}) loss_dict.update({f'{prefix}/loss_diff_light_lpips': loss_diff_light_lpips.item()}) loss_dict.update({f'{prefix}/loss_reg_sdf': sdf_reg_loss_entropy.item()}) loss_dict.update({f'{prefix}/loss_reg_surface': flexicubes_surface_reg.item()}) loss_dict.update({f'{prefix}/loss_reg_weights': flexicubes_weights_reg.item()}) if self.use_tv_loss: loss_dict.update({f'{prefix}/loss_tv': tv_loss.item()}) loss_dict.update({f'{prefix}/loss': loss.item()}) return loss, loss_dict @torch.no_grad() def validation_step(self, batch, batch_idx): lrm_generator_input = self.prepare_validation_batch_data(batch) render_out = self.forward(lrm_generator_input) render_images = rearrange(render_out['pbr_img'], 'b n c h w -> b c h (n w)') render_albodos = rearrange(render_out['img'], 'b n c h w -> b c h (n w)') self.validation_step_outputs.append(render_images) self.validation_step_outputs.append(render_albodos) def on_validation_epoch_end(self): images = torch.cat(self.validation_step_outputs, dim=0) all_images = self.all_gather(images) all_images = rearrange(all_images, 'r b c h w -> (r b) c h w') if self.global_rank == 0: image_path = os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png') grid = make_grid(all_images, nrow=1, normalize=True, value_range=(0, 1)) save_image(grid, image_path) print(f"Saved image to {image_path}") self.validation_step_outputs.clear() def configure_optimizers(self): lr = self.learning_rate optimizer = torch.optim.AdamW( self.lrm_generator.parameters(), lr=lr, betas=(0.90, 0.95), weight_decay=0.01) scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 100000, eta_min=0) return {'optimizer': optimizer, 'lr_scheduler': scheduler}