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_ortho_rays import systems from systems.base import BaseSystem from systems.criterions import PSNR, binary_cross_entropy import pdb def ranking_loss(error, penalize_ratio=0.7, extra_weights=None , type='mean'): error, indices = torch.sort(error) # only sum relatively small errors s_error = torch.index_select(error, 0, index=indices[:int(penalize_ratio * indices.shape[0])]) if extra_weights is not None: weights = torch.index_select(extra_weights, 0, index=indices[:int(penalize_ratio * indices.shape[0])]) s_error = s_error * weights if type == 'mean': return torch.mean(s_error) elif type == 'sum': return torch.sum(s_error) @systems.register('ortho-neus-system') class OrthoNeuSSystem(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 self.cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6) 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] origins = self.dataset.origins[y, x] elif self.dataset.directions.ndim == 4: # (N, H, W, 3) directions = self.dataset.directions[index, y, x] origins = self.dataset.origins[index, y, x] rays_o, rays_d = get_ortho_rays(origins, directions, c2w) rgb = self.dataset.all_images[index, y, x].view(-1, self.dataset.all_images.shape[-1]).to(self.rank) normal = self.dataset.all_normals_world[index, y, x].view(-1, self.dataset.all_normals_world.shape[-1]).to(self.rank) fg_mask = self.dataset.all_fg_masks[index, y, x].view(-1).to(self.rank) rgb_mask = self.dataset.all_rgb_masks[index, y, x].view(-1).to(self.rank) view_weights = self.dataset.view_weights[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 origins = self.dataset.origins elif self.dataset.directions.ndim == 4: # (N, H, W, 3) directions = self.dataset.directions[index][0] origins = self.dataset.origins[index][0] rays_o, rays_d = get_ortho_rays(origins, directions, c2w) rgb = self.dataset.all_images[index].view(-1, self.dataset.all_images.shape[-1]).to(self.rank) normal = self.dataset.all_normals_world[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) rgb_mask = self.dataset.all_rgb_masks[index].view(-1).to(self.rank) view_weights = None cosines = self.cos(rays_d, normal) 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 == 'black': self.model.background_color = torch.zeros((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, 'normal': normal, 'fg_mask': fg_mask, 'rgb_mask': rgb_mask, 'cosines': cosines, 'view_weights': view_weights }) def training_step(self, batch, batch_idx): out = self(batch) cosines = batch['cosines'] fg_mask = batch['fg_mask'] rgb_mask = batch['rgb_mask'] view_weights = batch['view_weights'] cosines[cosines > -0.1] = 0 mask = ((fg_mask > 0) & (cosines < -0.1)) rgb_mask = out['rays_valid_full'][...,0] & (rgb_mask > 0) grad_cosines = self.cos(batch['rays'][...,3:], out['comp_normal']).detach() # grad_cosines = cosines 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) erros_rgb_mse = F.mse_loss(out['comp_rgb_full'][rgb_mask], batch['rgb'][rgb_mask], reduction='none') # erros_rgb_mse = erros_rgb_mse * torch.exp(grad_cosines.abs())[:, None][rgb_mask] / torch.exp(grad_cosines.abs()[rgb_mask]).sum() # loss_rgb_mse = ranking_loss(erros_rgb_mse.sum(dim=1), penalize_ratio=0.7, type='sum') loss_rgb_mse = ranking_loss(erros_rgb_mse.sum(dim=1), penalize_ratio=self.config.system.loss.rgb_p_ratio, type='mean') self.log('train/loss_rgb_mse', loss_rgb_mse, prog_bar=True, rank_zero_only=True) loss += loss_rgb_mse * self.C(self.config.system.loss.lambda_rgb_mse) loss_rgb_l1 = F.l1_loss(out['comp_rgb_full'][rgb_mask], batch['rgb'][rgb_mask], reduction='none') loss_rgb_l1 = ranking_loss(loss_rgb_l1.sum(dim=1), # extra_weights=view_weights[rgb_mask], penalize_ratio=0.8) self.log('train/loss_rgb', loss_rgb_l1) loss += loss_rgb_l1 * self.C(self.config.system.loss.lambda_rgb_l1) normal_errors = 1 - F.cosine_similarity(out['comp_normal'], batch['normal'], dim=1) # normal_errors = normal_errors * cosines.abs() / cosines.abs().sum() if self.config.system.loss.geo_aware: normal_errors = normal_errors * torch.exp(cosines.abs()) / torch.exp(cosines.abs()).sum() loss_normal = ranking_loss(normal_errors[mask], penalize_ratio=self.config.system.loss.normal_p_ratio, extra_weights=view_weights[mask], type='sum') else: loss_normal = ranking_loss(normal_errors[mask], penalize_ratio=self.config.system.loss.normal_p_ratio, extra_weights=view_weights[mask], type='mean') self.log('train/loss_normal', loss_normal, prog_bar=True, rank_zero_only=True) loss += loss_normal * self.C(self.config.system.loss.lambda_normal) loss_eikonal = ((torch.linalg.norm(out['sdf_grad_samples'], ord=2, dim=-1) - 1.)**2).mean() self.log('train/loss_eikonal', loss_eikonal, prog_bar=True, rank_zero_only=True) 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(), reduction='none') loss_mask = ranking_loss(loss_mask, penalize_ratio=self.config.system.loss.mask_p_ratio, extra_weights=view_weights) self.log('train/loss_mask', loss_mask, prog_bar=True, rank_zero_only=True) 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['random_sdf'].abs()).mean() self.log('train/loss_sparsity', loss_sparsity, prog_bar=True, rank_zero_only=True) 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) if self.C(self.config.system.loss.lambda_3d_normal_smooth) > 0: if "random_sdf_grad" not in out: raise ValueError( "random_sdf_grad is required for normal smooth loss, no normal is found in the output." ) if "normal_perturb" not in out: raise ValueError( "normal_perturb is required for normal smooth loss, no normal_perturb is found in the output." ) normals_3d = out["random_sdf_grad"] normals_perturb_3d = out["normal_perturb"] loss_3d_normal_smooth = (normals_3d - normals_perturb_3d).abs().mean() self.log('train/loss_3d_normal_smooth', loss_3d_normal_smooth, prog_bar=True ) loss += loss_3d_normal_smooth * self.C(self.config.system.loss.lambda_3d_normal_smooth) 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) self.export() # 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_step(self, batch, batch_idx): pass 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) # pdb.set_trace() self.save_mesh( f"it{self.global_step}-{self.config.model.geometry.isosurface.method}{self.config.model.geometry.isosurface.resolution}.obj", ortho_scale=self.config.export.ortho_scale, **mesh )