import torch import torch.nn as nn import torch.nn.functional as F from torch_efficient_distloss import flatten_eff_distloss import pytorch_lightning as pl from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_debug import models from models.utils import cleanup from models.ray_utils import get_rays import systems from systems.base import BaseSystem from systems.criterions import PSNR, binary_cross_entropy @systems.register('neus-system') class NeuSSystem(BaseSystem): """ Two ways to print to console: 1. self.print: correctly handle progress bar 2. rank_zero_info: use the logging module """ def prepare(self): self.criterions = { 'psnr': PSNR() } self.train_num_samples = self.config.model.train_num_rays * (self.config.model.num_samples_per_ray + self.config.model.get('num_samples_per_ray_bg', 0)) self.train_num_rays = self.config.model.train_num_rays def forward(self, batch): return self.model(batch['rays']) def preprocess_data(self, batch, stage): if 'index' in batch: # validation / testing index = batch['index'] else: if self.config.model.batch_image_sampling: index = torch.randint(0, len(self.dataset.all_images), size=(self.train_num_rays,), device=self.dataset.all_images.device) else: index = torch.randint(0, len(self.dataset.all_images), size=(1,), device=self.dataset.all_images.device) if stage in ['train']: c2w = self.dataset.all_c2w[index] x = torch.randint( 0, self.dataset.w, size=(self.train_num_rays,), device=self.dataset.all_images.device ) y = torch.randint( 0, self.dataset.h, size=(self.train_num_rays,), device=self.dataset.all_images.device ) if self.dataset.directions.ndim == 3: # (H, W, 3) directions = self.dataset.directions[y, x] elif self.dataset.directions.ndim == 4: # (N, H, W, 3) directions = self.dataset.directions[index, y, x] rays_o, rays_d = get_rays(directions, c2w) rgb = self.dataset.all_images[index, y, x].view(-1, self.dataset.all_images.shape[-1]).to(self.rank) fg_mask = self.dataset.all_fg_masks[index, y, x].view(-1).to(self.rank) else: c2w = self.dataset.all_c2w[index][0] if self.dataset.directions.ndim == 3: # (H, W, 3) directions = self.dataset.directions elif self.dataset.directions.ndim == 4: # (N, H, W, 3) directions = self.dataset.directions[index][0] rays_o, rays_d = get_rays(directions, c2w) rgb = self.dataset.all_images[index].view(-1, self.dataset.all_images.shape[-1]).to(self.rank) fg_mask = self.dataset.all_fg_masks[index].view(-1).to(self.rank) rays = torch.cat([rays_o, F.normalize(rays_d, p=2, dim=-1)], dim=-1) if stage in ['train']: if self.config.model.background_color == 'white': self.model.background_color = torch.ones((3,), dtype=torch.float32, device=self.rank) elif self.config.model.background_color == 'random': self.model.background_color = torch.rand((3,), dtype=torch.float32, device=self.rank) else: raise NotImplementedError else: self.model.background_color = torch.ones((3,), dtype=torch.float32, device=self.rank) if self.dataset.apply_mask: rgb = rgb * fg_mask[...,None] + self.model.background_color * (1 - fg_mask[...,None]) batch.update({ 'rays': rays, 'rgb': rgb, 'fg_mask': fg_mask }) def training_step(self, batch, batch_idx): out = self(batch) loss = 0. # update train_num_rays if self.config.model.dynamic_ray_sampling: train_num_rays = int(self.train_num_rays * (self.train_num_samples / out['num_samples_full'].sum().item())) self.train_num_rays = min(int(self.train_num_rays * 0.9 + train_num_rays * 0.1), self.config.model.max_train_num_rays) loss_rgb_mse = F.mse_loss(out['comp_rgb_full'][out['rays_valid_full'][...,0]], batch['rgb'][out['rays_valid_full'][...,0]]) self.log('train/loss_rgb_mse', loss_rgb_mse) loss += loss_rgb_mse * self.C(self.config.system.loss.lambda_rgb_mse) loss_rgb_l1 = F.l1_loss(out['comp_rgb_full'][out['rays_valid_full'][...,0]], batch['rgb'][out['rays_valid_full'][...,0]]) self.log('train/loss_rgb', loss_rgb_l1) loss += loss_rgb_l1 * self.C(self.config.system.loss.lambda_rgb_l1) loss_eikonal = ((torch.linalg.norm(out['sdf_grad_samples'], ord=2, dim=-1) - 1.)**2).mean() self.log('train/loss_eikonal', loss_eikonal) loss += loss_eikonal * self.C(self.config.system.loss.lambda_eikonal) opacity = torch.clamp(out['opacity'].squeeze(-1), 1.e-3, 1.-1.e-3) loss_mask = binary_cross_entropy(opacity, batch['fg_mask'].float()) self.log('train/loss_mask', loss_mask) loss += loss_mask * (self.C(self.config.system.loss.lambda_mask) if self.dataset.has_mask else 0.0) loss_opaque = binary_cross_entropy(opacity, opacity) self.log('train/loss_opaque', loss_opaque) loss += loss_opaque * self.C(self.config.system.loss.lambda_opaque) loss_sparsity = torch.exp(-self.config.system.loss.sparsity_scale * out['sdf_samples'].abs()).mean() self.log('train/loss_sparsity', loss_sparsity) loss += loss_sparsity * self.C(self.config.system.loss.lambda_sparsity) if self.C(self.config.system.loss.lambda_curvature) > 0: assert 'sdf_laplace_samples' in out, "Need geometry.grad_type='finite_difference' to get SDF Laplace samples" loss_curvature = out['sdf_laplace_samples'].abs().mean() self.log('train/loss_curvature', loss_curvature) loss += loss_curvature * self.C(self.config.system.loss.lambda_curvature) # distortion loss proposed in MipNeRF360 # an efficient implementation from https://github.com/sunset1995/torch_efficient_distloss if self.C(self.config.system.loss.lambda_distortion) > 0: loss_distortion = flatten_eff_distloss(out['weights'], out['points'], out['intervals'], out['ray_indices']) self.log('train/loss_distortion', loss_distortion) loss += loss_distortion * self.C(self.config.system.loss.lambda_distortion) if self.config.model.learned_background and self.C(self.config.system.loss.lambda_distortion_bg) > 0: loss_distortion_bg = flatten_eff_distloss(out['weights_bg'], out['points_bg'], out['intervals_bg'], out['ray_indices_bg']) self.log('train/loss_distortion_bg', loss_distortion_bg) loss += loss_distortion_bg * self.C(self.config.system.loss.lambda_distortion_bg) losses_model_reg = self.model.regularizations(out) for name, value in losses_model_reg.items(): self.log(f'train/loss_{name}', value) loss_ = value * self.C(self.config.system.loss[f"lambda_{name}"]) loss += loss_ self.log('train/inv_s', out['inv_s'], prog_bar=True) for name, value in self.config.system.loss.items(): if name.startswith('lambda'): self.log(f'train_params/{name}', self.C(value)) self.log('train/num_rays', float(self.train_num_rays), prog_bar=True) return { 'loss': loss } """ # aggregate outputs from different devices (DP) def training_step_end(self, out): pass """ """ # aggregate outputs from different iterations def training_epoch_end(self, out): pass """ def validation_step(self, batch, batch_idx): out = self(batch) psnr = self.criterions['psnr'](out['comp_rgb_full'].to(batch['rgb']), batch['rgb']) W, H = self.dataset.img_wh self.save_image_grid(f"it{self.global_step}-{batch['index'][0].item()}.png", [ {'type': 'rgb', 'img': batch['rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}, {'type': 'rgb', 'img': out['comp_rgb_full'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}} ] + ([ {'type': 'rgb', 'img': out['comp_rgb_bg'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}, {'type': 'rgb', 'img': out['comp_rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}, ] if self.config.model.learned_background else []) + [ {'type': 'grayscale', 'img': out['depth'].view(H, W), 'kwargs': {}}, {'type': 'rgb', 'img': out['comp_normal'].view(H, W, 3), 'kwargs': {'data_format': 'HWC', 'data_range': (-1, 1)}} ]) return { 'psnr': psnr, 'index': batch['index'] } """ # aggregate outputs from different devices when using DP def validation_step_end(self, out): pass """ def validation_epoch_end(self, out): out = self.all_gather(out) if self.trainer.is_global_zero: out_set = {} for step_out in out: # DP if step_out['index'].ndim == 1: out_set[step_out['index'].item()] = {'psnr': step_out['psnr']} # DDP else: for oi, index in enumerate(step_out['index']): out_set[index[0].item()] = {'psnr': step_out['psnr'][oi]} psnr = torch.mean(torch.stack([o['psnr'] for o in out_set.values()])) self.log('val/psnr', psnr, prog_bar=True, rank_zero_only=True) def test_step(self, batch, batch_idx): out = self(batch) psnr = self.criterions['psnr'](out['comp_rgb_full'].to(batch['rgb']), batch['rgb']) W, H = self.dataset.img_wh self.save_image_grid(f"it{self.global_step}-test/{batch['index'][0].item()}.png", [ {'type': 'rgb', 'img': batch['rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}, {'type': 'rgb', 'img': out['comp_rgb_full'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}} ] + ([ {'type': 'rgb', 'img': out['comp_rgb_bg'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}, {'type': 'rgb', 'img': out['comp_rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}, ] if self.config.model.learned_background else []) + [ {'type': 'grayscale', 'img': out['depth'].view(H, W), 'kwargs': {}}, {'type': 'rgb', 'img': out['comp_normal'].view(H, W, 3), 'kwargs': {'data_format': 'HWC', 'data_range': (-1, 1)}} ]) return { 'psnr': psnr, 'index': batch['index'] } def test_epoch_end(self, out): """ Synchronize devices. Generate image sequence using test outputs. """ out = self.all_gather(out) if self.trainer.is_global_zero: out_set = {} for step_out in out: # DP if step_out['index'].ndim == 1: out_set[step_out['index'].item()] = {'psnr': step_out['psnr']} # DDP else: for oi, index in enumerate(step_out['index']): out_set[index[0].item()] = {'psnr': step_out['psnr'][oi]} psnr = torch.mean(torch.stack([o['psnr'] for o in out_set.values()])) self.log('test/psnr', psnr, prog_bar=True, rank_zero_only=True) self.save_img_sequence( f"it{self.global_step}-test", f"it{self.global_step}-test", '(\d+)\.png', save_format='mp4', fps=30 ) self.export() def export(self): mesh = self.model.export(self.config.export) self.save_mesh( f"it{self.global_step}-{self.config.model.geometry.isosurface.method}{self.config.model.geometry.isosurface.resolution}.obj", **mesh )