import os import numpy as np import torch import torch.nn.functional as F 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.train_util import instantiate_from_config # 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 class MVRecon(pl.LightningModule): def __init__( self, lrm_generator_config, input_size=256, render_size=512, init_ckpt=None, ): super(MVRecon, self).__init__() self.input_size = input_size self.render_size = render_size # init modules self.lrm_generator = instantiate_from_config(lrm_generator_config) self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg') # Load weights from pretrained MVRecon model, and use the mlp # weights to initialize the weights of sdf and rgb mlps. 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.')] = 3.0 - 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.global_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 prepare_batch_data(self, batch): 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) 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'] render_c2ws = torch.cat([input_c2ws, target_c2ws], dim=1) render_w2cs = torch.linalg.inv(render_c2ws) input_extrinsics = input_c2ws.flatten(-2) input_extrinsics = input_extrinsics[:, :, :12] input_intrinsics = input_Ks.flatten(-2) 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) # target images target_images = torch.cat([batch['input_images'], batch['target_images']], 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) 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_images'] = target_images.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) 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) # 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'] render_w2cs = torch.linalg.inv(render_c2ws) 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, render_cameras, render_size=512): planes = torch.utils.checkpoint.checkpoint( self.lrm_generator.forward_planes, images, cameras, use_reentrant=False, ) out = self.lrm_generator.forward_geometry( planes, render_cameras, render_size, ) return out def forward(self, lrm_generator_input): images = lrm_generator_input['images'] cameras = lrm_generator_input['cameras'] render_cameras = lrm_generator_input['render_cameras'] render_size = lrm_generator_input['render_size'] out = self.forward_lrm_generator( images, cameras, render_cameras, render_size=render_size) return out def training_step(self, batch, batch_idx): lrm_generator_input, render_gt = self.prepare_batch_data(batch) render_out = self.forward(lrm_generator_input) 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) if self.global_step % 1000 == 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['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)') render_alphas = rearrange( repeat(render_out['mask'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') target_depths = rearrange( repeat(render_gt['target_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') render_depths = rearrange( repeat(render_out['depth'], 'b n 1 h w -> b n 3 h w'), '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)') render_normals = rearrange( render_out['normal'], '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 grid = torch.cat([ target_images, render_images, target_alphas, render_alphas, target_depths, render_depths, target_normals, render_normals, ], dim=-2) 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 compute_loss(self, render_out, render_gt): # NOTE: the rgb value range of OpenLRM is [0, 1] render_images = render_out['img'] target_images = render_gt['target_images'].to(render_images) render_images = rearrange(render_images, 'b n ... -> (b n) ...') * 2.0 - 1.0 target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0 loss_mse = F.mse_loss(render_images, target_images) loss_lpips = 2.0 * self.lpips(render_images, target_images) render_alphas = render_out['mask'] target_alphas = render_gt['target_alphas'] loss_mask = F.mse_loss(render_alphas, target_alphas) render_depths = render_out['depth'] target_depths = render_gt['target_depths'] loss_depth = 0.5 * F.l1_loss(render_depths[target_alphas>0], target_depths[target_alphas>0]) render_normals = render_out['normal'] * 2.0 - 1.0 target_normals = render_gt['target_normals'] * 2.0 - 1.0 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 # 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 = loss_mse + loss_lpips + loss_mask + loss_normal + loss_reg prefix = 'train' loss_dict = {} loss_dict.update({f'{prefix}/loss_mse': loss_mse}) loss_dict.update({f'{prefix}/loss_lpips': loss_lpips}) loss_dict.update({f'{prefix}/loss_mask': loss_mask}) loss_dict.update({f'{prefix}/loss_normal': loss_normal}) loss_dict.update({f'{prefix}/loss_depth': loss_depth}) loss_dict.update({f'{prefix}/loss_reg_sdf': sdf_reg_loss_entropy}) loss_dict.update({f'{prefix}/loss_reg_surface': flexicubes_surface_reg}) loss_dict.update({f'{prefix}/loss_reg_weights': flexicubes_weights_reg}) loss_dict.update({f'{prefix}/loss': loss}) 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 = render_out['img'] render_images = rearrange(render_images, 'b n c h w -> b c h (n w)') self.validation_step_outputs.append(render_images) def on_validation_epoch_end(self): images = torch.cat(self.validation_step_outputs, dim=-1) 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}