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 class MVRecon(pl.LightningModule): def __init__( self, lrm_generator_config, lrm_path=None, input_size=256, render_size=192, ): 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) if lrm_path is not None: lrm_ckpt = torch.load(lrm_path) self.lrm_generator.load_state_dict(lrm_ckpt['weights'], strict=False) self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg') self.validation_step_outputs = [] def on_fit_start(self): 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 = {} # for supervision # 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'].flatten(-2) input_Ks = batch['input_Ks'].flatten(-2) target_c2ws = batch['target_c2ws'].flatten(-2) target_Ks = batch['target_Ks'].flatten(-2) render_cameras_input = torch.cat([input_c2ws, input_Ks], dim=-1) render_cameras_target = torch.cat([target_c2ws, target_Ks], dim=-1) render_cameras = torch.cat([render_cameras_input, render_cameras_target], dim=1) 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) # 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_cameras.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) # random crop render_size = np.random.randint(self.render_size, 513) 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) crop_params = v2.RandomCrop.get_params( target_images, output_size=(self.render_size, self.render_size)) target_images = v2.functional.crop(target_images, *crop_params) target_depths = v2.functional.crop(target_depths, *crop_params)[:, :, 0:1] target_alphas = v2.functional.crop(target_alphas, *crop_params)[:, :, 0:1] lrm_generator_input['render_size'] = render_size lrm_generator_input['crop_params'] = crop_params 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) 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_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_c2ws = batch['render_c2ws'].flatten(-2) render_Ks = batch['render_Ks'].flatten(-2) render_cameras = torch.cat([render_c2ws, render_Ks], dim=-1) lrm_generator_input['render_cameras'] = render_cameras.to(self.device) lrm_generator_input['render_size'] = 384 lrm_generator_input['crop_params'] = None return lrm_generator_input def forward_lrm_generator( self, images, cameras, render_cameras, render_size=192, crop_params=None, chunk_size=1, ): planes = torch.utils.checkpoint.checkpoint( self.lrm_generator.forward_planes, images, cameras, use_reentrant=False, ) frames = [] for i in range(0, render_cameras.shape[1], chunk_size): frames.append( torch.utils.checkpoint.checkpoint( self.lrm_generator.synthesizer, planes, cameras=render_cameras[:, i:i+chunk_size], render_size=render_size, crop_params=crop_params, use_reentrant=False ) ) frames = { k: torch.cat([r[k] for r in frames], dim=1) for k in frames[0].keys() } return frames 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'] crop_params = lrm_generator_input['crop_params'] out = self.forward_lrm_generator( images, cameras, render_cameras, render_size=render_size, crop_params=crop_params, chunk_size=1, ) render_images = torch.clamp(out['images_rgb'], 0.0, 1.0) render_depths = out['images_depth'] render_alphas = torch.clamp(out['images_weight'], 0.0, 1.0) out = { 'render_images': render_images, 'render_depths': render_depths, 'render_alphas': render_alphas, } 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] input_images = v2.functional.resize( lrm_generator_input['images'], (H, W), interpolation=3, antialias=True).clamp(0, 1) input_images = torch.cat( [input_images, torch.ones(B, N-N_in, C, H, W).to(input_images)], dim=1) input_images = rearrange( input_images, 'b n c h w -> b c h (n w)') target_images = rearrange( render_gt['target_images'], 'b n c h w -> b c h (n w)') render_images = rearrange( render_out['render_images'], '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['render_alphas'], '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['render_depths'], 'b n 1 h w -> b n 3 h w'), '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([ input_images, target_images, render_images, target_alphas, render_alphas, target_depths, render_depths, ], dim=-2) grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1)) save_image(grid, os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png')) return loss def compute_loss(self, render_out, render_gt): # NOTE: the rgb value range of OpenLRM is [0, 1] render_images = render_out['render_images'] 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['render_alphas'] target_alphas = render_gt['target_alphas'] loss_mask = F.mse_loss(render_alphas, target_alphas) loss = loss_mse + loss_lpips + loss_mask 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': 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['render_images'] 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 params = [] lrm_params_fast, lrm_params_slow = [], [] for n, p in self.lrm_generator.named_parameters(): if 'adaLN_modulation' in n or 'camera_embedder' in n: lrm_params_fast.append(p) else: lrm_params_slow.append(p) params.append({"params": lrm_params_fast, "lr": lr, "weight_decay": 0.01 }) params.append({"params": lrm_params_slow, "lr": lr / 10.0, "weight_decay": 0.01 }) optimizer = torch.optim.AdamW(params, lr=lr, betas=(0.90, 0.95)) scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 3000, eta_min=lr/4) return {'optimizer': optimizer, 'lr_scheduler': scheduler}