trellis/trainers/vae/structured_latent_vae_gaussian.py

from typing import *
import copy
import torch
from torch.utils.data import DataLoader
import numpy as np
from easydict import EasyDict as edict
import utils3d.torch

from ..basic import BasicTrainer
from ...representations import Gaussian
from ...renderers import GaussianRenderer
from ...modules.sparse import SparseTensor
from ...utils.loss_utils import l1_loss, l2_loss, ssim, lpips


class SLatVaeGaussianTrainer(BasicTrainer):
    """
    Trainer for structured latent VAE.
    
    Args:
        models (dict[str, nn.Module]): Models to train.
        dataset (torch.utils.data.Dataset): Dataset.
        output_dir (str): Output directory.
        load_dir (str): Load directory.
        step (int): Step to load.
        batch_size (int): Batch size.
        batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
        batch_split (int): Split batch with gradient accumulation.
        max_steps (int): Max steps.
        optimizer (dict): Optimizer config.
        lr_scheduler (dict): Learning rate scheduler config.
        elastic (dict): Elastic memory management config.
        grad_clip (float or dict): Gradient clip config.
        ema_rate (float or list): Exponential moving average rates.
        fp16_mode (str): FP16 mode.
            - None: No FP16.
            - 'inflat_all': Hold a inflated fp32 master param for all params.
            - 'amp': Automatic mixed precision.
        fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
        finetune_ckpt (dict): Finetune checkpoint.
        log_param_stats (bool): Log parameter stats.
        i_print (int): Print interval.
        i_log (int): Log interval.
        i_sample (int): Sample interval.
        i_save (int): Save interval.
        i_ddpcheck (int): DDP check interval.
        
        loss_type (str): Loss type. Can be 'l1', 'l2'
        lambda_ssim (float): SSIM loss weight.
        lambda_lpips (float): LPIPS loss weight.
        lambda_kl (float): KL loss weight.
        regularizations (dict): Regularization config.
    """
    
    def __init__(
        self,
        *args,
        loss_type: str = 'l1',
        lambda_ssim: float = 0.2,
        lambda_lpips: float = 0.2,
        lambda_kl: float = 1e-6,
        regularizations: Dict = {},
        **kwargs
    ):
        super().__init__(*args, **kwargs)
        self.loss_type = loss_type
        self.lambda_ssim = lambda_ssim
        self.lambda_lpips = lambda_lpips
        self.lambda_kl = lambda_kl
        self.regularizations = regularizations
        
        self._init_renderer()
        
    def _init_renderer(self):
        rendering_options = {"near" : 0.8,
                             "far" : 1.6,
                             "bg_color" : 'random'}
        self.renderer = GaussianRenderer(rendering_options)
        self.renderer.pipe.kernel_size = self.models['decoder'].rep_config['2d_filter_kernel_size']
        
    def _render_batch(self, reps: List[Gaussian], extrinsics: torch.Tensor, intrinsics: torch.Tensor) -> torch.Tensor:
        """
        Render a batch of representations.

        Args:
            reps: The dictionary of lists of representations.
            extrinsics: The [N x 4 x 4] tensor of extrinsics.
            intrinsics: The [N x 3 x 3] tensor of intrinsics.
        """
        ret = None
        for i, representation in enumerate(reps):
            render_pack = self.renderer.render(representation, extrinsics[i], intrinsics[i])
            if ret is None:
                ret = {k: [] for k in list(render_pack.keys()) + ['bg_color']}
            for k, v in render_pack.items():
                ret[k].append(v)
            ret['bg_color'].append(self.renderer.bg_color)
        for k, v in ret.items():
            ret[k] = torch.stack(v, dim=0) 
        return ret
        
    @torch.no_grad()
    def _get_status(self, z: SparseTensor, reps: List[Gaussian]) -> Dict:
        xyz = torch.cat([g.get_xyz for g in reps], dim=0)
        xyz_base = (z.coords[:, 1:].float() + 0.5) / self.models['decoder'].resolution - 0.5
        offset = xyz - xyz_base.unsqueeze(1).expand(-1, self.models['decoder'].rep_config['num_gaussians'], -1).reshape(-1, 3)
        status = {
            'xyz': xyz,
            'offset': offset,
            'scale': torch.cat([g.get_scaling for g in reps], dim=0),
            'opacity': torch.cat([g.get_opacity for g in reps], dim=0),
        }

        for k in list(status.keys()):
            status[k] = {
                'mean': status[k].mean().item(),
                'max': status[k].max().item(),
                'min': status[k].min().item(),
            }
            
        return status
    
    def _get_regularization_loss(self, reps: List[Gaussian]) -> Tuple[torch.Tensor, Dict]:
        loss = 0.0
        terms = {}
        if 'lambda_vol' in self.regularizations:
            scales = torch.cat([g.get_scaling for g in reps], dim=0)   # [N x 3]
            volume = torch.prod(scales, dim=1)  # [N]
            terms[f'reg_vol'] = volume.mean()
            loss = loss + self.regularizations['lambda_vol'] * terms[f'reg_vol']
        if 'lambda_opacity' in self.regularizations:
            opacity = torch.cat([g.get_opacity for g in reps], dim=0)
            terms[f'reg_opacity'] = (opacity - 1).pow(2).mean()
            loss = loss + self.regularizations['lambda_opacity'] * terms[f'reg_opacity']
        return loss, terms
    
    def training_losses(
        self,
        feats: SparseTensor,
        image: torch.Tensor,
        alpha: torch.Tensor,
        extrinsics: torch.Tensor,
        intrinsics: torch.Tensor,
        return_aux: bool = False,
        **kwargs
    ) -> Tuple[Dict, Dict]:
        """
        Compute training losses.

        Args:
            feats: The [N x * x C] sparse tensor of features.
            image: The [N x 3 x H x W] tensor of images.
            alpha: The [N x H x W] tensor of alpha channels.
            extrinsics: The [N x 4 x 4] tensor of extrinsics.
            intrinsics: The [N x 3 x 3] tensor of intrinsics.
            return_aux: Whether to return auxiliary information.

        Returns:
            a dict with the key "loss" containing a scalar tensor.
            may also contain other keys for different terms.
        """
        z, mean, logvar = self.training_models['encoder'](feats, sample_posterior=True, return_raw=True)
        reps = self.training_models['decoder'](z)
        self.renderer.rendering_options.resolution = image.shape[-1]
        render_results = self._render_batch(reps, extrinsics, intrinsics)     
        
        terms = edict(loss = 0.0, rec = 0.0)
        
        rec_image = render_results['color']
        gt_image = image * alpha[:, None] + (1 - alpha[:, None]) * render_results['bg_color'][..., None, None]
                
        if self.loss_type == 'l1':
            terms["l1"] = l1_loss(rec_image, gt_image)
            terms["rec"] = terms["rec"] + terms["l1"]
        elif self.loss_type == 'l2':
            terms["l2"] = l2_loss(rec_image, gt_image)
            terms["rec"] = terms["rec"] + terms["l2"]
        else:
            raise ValueError(f"Invalid loss type: {self.loss_type}")
        if self.lambda_ssim > 0:
            terms["ssim"] = 1 - ssim(rec_image, gt_image)
            terms["rec"] = terms["rec"] + self.lambda_ssim * terms["ssim"]
        if self.lambda_lpips > 0:
            terms["lpips"] = lpips(rec_image, gt_image)
            terms["rec"] = terms["rec"] + self.lambda_lpips * terms["lpips"]
        terms["loss"] = terms["loss"] + terms["rec"]

        terms["kl"] = 0.5 * torch.mean(mean.pow(2) + logvar.exp() - logvar - 1)
        terms["loss"] = terms["loss"] + self.lambda_kl * terms["kl"]
        
        reg_loss, reg_terms = self._get_regularization_loss(reps)
        terms.update(reg_terms)
        terms["loss"] = terms["loss"] + reg_loss
        
        status = self._get_status(z, reps)
        
        if return_aux:
            return terms, status, {'rec_image': rec_image, 'gt_image': gt_image}       
        return terms, status
    
    @torch.no_grad()
    def run_snapshot(
        self,
        num_samples: int,
        batch_size: int,
        verbose: bool = False,
    ) -> Dict:
        dataloader = DataLoader(
            copy.deepcopy(self.dataset),
            batch_size=batch_size,
            shuffle=True,
            num_workers=0,
            collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
        )

        # inference
        ret_dict = {}
        gt_images = []
        exts = []
        ints = []
        reps = []
        for i in range(0, num_samples, batch_size):
            batch = min(batch_size, num_samples - i)
            data = next(iter(dataloader))
            args = {k: v[:batch].cuda() for k, v in data.items()}
            gt_images.append(args['image'] * args['alpha'][:, None])
            exts.append(args['extrinsics'])
            ints.append(args['intrinsics'])
            z = self.models['encoder'](args['feats'], sample_posterior=True, return_raw=False)
            reps.extend(self.models['decoder'](z))
        gt_images = torch.cat(gt_images, dim=0)
        ret_dict.update({f'gt_image': {'value': gt_images, 'type': 'image'}})

        # render single view
        exts = torch.cat(exts, dim=0)
        ints = torch.cat(ints, dim=0)
        self.renderer.rendering_options.bg_color = (0, 0, 0)
        self.renderer.rendering_options.resolution = gt_images.shape[-1]
        render_results = self._render_batch(reps, exts, ints)
        ret_dict.update({f'rec_image': {'value': render_results['color'], 'type': 'image'}})

        # render multiview
        self.renderer.rendering_options.resolution = 512
        ## Build camera
        yaws = [0, np.pi / 2, np.pi, 3 * np.pi / 2]
        yaws_offset = np.random.uniform(-np.pi / 4, np.pi / 4)
        yaws = [y + yaws_offset for y in yaws]
        pitch = [np.random.uniform(-np.pi / 4, np.pi / 4) for _ in range(4)]

        ## render each view
        miltiview_images = []
        for yaw, pitch in zip(yaws, pitch):
            orig = torch.tensor([
                np.sin(yaw) * np.cos(pitch),
                np.cos(yaw) * np.cos(pitch),
                np.sin(pitch),
            ]).float().cuda() * 2
            fov = torch.deg2rad(torch.tensor(30)).cuda()
            extrinsics = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda())
            intrinsics = utils3d.torch.intrinsics_from_fov_xy(fov, fov)
            extrinsics = extrinsics.unsqueeze(0).expand(num_samples, -1, -1)
            intrinsics = intrinsics.unsqueeze(0).expand(num_samples, -1, -1)
            render_results = self._render_batch(reps, extrinsics, intrinsics)
            miltiview_images.append(render_results['color'])

        ## Concatenate views
        miltiview_images = torch.cat([
            torch.cat(miltiview_images[:2], dim=-2),
            torch.cat(miltiview_images[2:], dim=-2),
        ], dim=-1)
        ret_dict.update({f'miltiview_image': {'value': miltiview_images, 'type': 'image'}})

        self.renderer.rendering_options.bg_color = 'random'
                                    
        return ret_dict