""" Modified from: https://github.com/NVlabs/LSGM/blob/main/training_obj_joint.py """ import copy import functools import json import os from pathlib import Path from pdb import set_trace as st from typing import Any import blobfile as bf import imageio import numpy as np import torch as th import torch.distributed as dist import torchvision from PIL import Image from torch.nn.parallel.distributed import DistributedDataParallel as DDP from torch.optim import AdamW from torch.utils.tensorboard.writer import SummaryWriter from tqdm import tqdm from guided_diffusion import dist_util, logger from guided_diffusion.fp16_util import MixedPrecisionTrainer from guided_diffusion.nn import update_ema from guided_diffusion.resample import LossAwareSampler, UniformSampler # from .train_util import TrainLoop3DRec from guided_diffusion.train_util import (TrainLoop, calc_average_loss, find_ema_checkpoint, find_resume_checkpoint, get_blob_logdir, log_loss_dict, log_rec3d_loss_dict, parse_resume_step_from_filename) from guided_diffusion.gaussian_diffusion import ModelMeanType from dnnlib.util import requires_grad from dnnlib.util import calculate_adaptive_weight from ..train_util_diffusion import TrainLoop3DDiffusion, TrainLoopDiffusionWithRec from ..cvD.nvsD_canoD import TrainLoop3DcvD_nvsD_canoD from guided_diffusion.continuous_diffusion_utils import get_mixed_prediction, different_p_q_objectives, kl_per_group_vada, kl_balancer # import utils as lsgm_utils class JointDenoiseRecModel(th.nn.Module): def __init__(self, ddpm_model, rec_model, diffusion_input_size) -> None: super().__init__() # del ddpm_model # th.cuda.empty_cache() # self.ddpm_model = th.nn.Identity() self.ddpm_model = ddpm_model self.rec_model = rec_model self._setup_latent_stat(diffusion_input_size) def _setup_latent_stat(self, diffusion_input_size): # for dynamic EMA tracking. latent_size = ( 1, self.ddpm_model.in_channels, # type: ignore diffusion_input_size, diffusion_input_size), self.ddpm_model.register_buffer( 'ema_latent_std', th.ones(*latent_size).to(dist_util.dev()), persistent=True) self.ddpm_model.register_buffer( 'ema_latent_mean', th.zeros(*latent_size).to(dist_util.dev()), persistent=True) # TODO, lint api. def forward( self, *args, model_name='ddpm', **kwargs, ): if model_name == 'ddpm': return self.ddpm_model(*args, **kwargs) elif model_name == 'rec': return self.rec_model(*args, **kwargs) else: raise NotImplementedError(model_name) # TODO, merge with train_util_diffusion.py later class SDETrainLoopJoint(TrainLoopDiffusionWithRec): """A dataclass with some required attribtues; copied from guided_diffusion TrainLoop """ def __init__( self, rec_model, denoise_model, diffusion, # not used sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, train_vae=False, **kwargs, ) -> None: joint_model = JointDenoiseRecModel(denoise_model, rec_model, diffusion_input_size) super().__init__( model=joint_model, diffusion=diffusion, # just for sampling loss_class=loss_class, data=data, eval_data=eval_data, eval_interval=eval_interval, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, use_amp=use_amp, model_name='joint_denoise_rec_model', iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, diffusion_input_size=diffusion_input_size, train_vae=train_vae, **kwargs) self.sde_diffusion = sde_diffusion # setup latent scaling factor # ! integrate the init_params_group for rec model def _setup_model(self): super()._setup_model() self.ddp_rec_model = functools.partial(self.model, model_name='rec') self.ddp_ddpm_model = functools.partial(self.model, model_name='ddpm') self.rec_model = self.ddp_model.module.rec_model self.ddpm_model = self.ddp_model.module.ddpm_model # compatability # TODO, required? # for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters( # type: ignore # ): # type: ignore # param.requires_grad_( # False # ) # ! disable triplane_decoder grad in each iteration indepenently; def _load_model(self): # TODO, for currently compatability if 'joint' in self.resume_checkpoint: # load joint directly self._load_and_sync_parameters(model=self.model, model_name=self.model_name) else: # from scratch self._load_and_sync_parameters(model=self.rec_model, model_name='rec') self._load_and_sync_parameters(model=self.ddpm_model, model_name='ddpm') def _setup_opt(self): # TODO, two optims groups. self.opt = AdamW([{ 'name': 'ddpm', 'params': self.ddpm_model.parameters(), }], lr=self.lr, weight_decay=self.weight_decay) if self.train_vae: for rec_param_group in self._init_optim_groups(self.rec_model): self.opt.add_param_group(rec_param_group) print(self.opt) class TrainLoop3DDiffusionLSGMJointnoD(SDETrainLoopJoint): def __init__(self, *, rec_model, denoise_model, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, resume_cldm_checkpoint=None, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, diffusion_ce_anneal=False, # compile=False, **kwargs): super().__init__(rec_model=rec_model, denoise_model=denoise_model, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, **kwargs) if sde_diffusion is not None: sde_diffusion.args.batch_size = batch_size self.latent_name = 'latent_normalized_2Ddiffusion' # normalized triplane latent self.render_latent_behaviour = 'decode_after_vae' # directly render using triplane operations self.diffusion_ce_anneal = diffusion_ce_anneal # assert sde_diffusion.args.train_vae def prepare_ddpm(self, eps, mode='p'): log_rec3d_loss_dict({ f'eps_mean': eps.mean(), f'eps_std': eps.std([1,2,3]).mean(0), f'eps_max': eps.max() }) args = self.sde_diffusion.args # sample noise noise = th.randn(size=eps.size(), device=eps.device ) # note that this noise value is currently shared! # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae) if mode == 'p': t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \ self.sde_diffusion.iw_quantities(args.iw_sample_p, noise.shape[0]) # TODO, q not used, fall back to original ddpm implementation else: assert mode == 'q' # assert args.iw_sample_q in ['ll_uniform', 'll_iw'] t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \ self.sde_diffusion.iw_quantities(args.iw_sample_q, noise.shape[0]) # TODO, q not used, fall back to original ddpm implementation eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p, m_t_p) # ! important # eps_t_p = eps_t_p.detach().requires_grad_(True) # logsnr_p = self.sde_diffusion.log_snr(m_t_p, # var_t_p) # for p only logsnr_p = self.sde_diffusion.log_snr(m_t_p, var_t_p) # for p only return { 'noise': noise, 't_p': t_p, 'eps_t_p': eps_t_p, 'logsnr_p': logsnr_p, 'obj_weight_t_p': obj_weight_t_p, 'var_t_p': var_t_p, 'm_t_p': m_t_p, 'eps': eps, 'mode': mode } # merged from noD.py def ce_weight(self): return self.loss_class.opt.ce_lambda def apply_model(self, p_sample_batch, **model_kwargs): args = self.sde_diffusion.args # args = self.sde_diffusion.args noise, eps_t_p, t_p, logsnr_p, obj_weight_t_p, var_t_p, m_t_p = ( p_sample_batch[k] for k in ('noise', 'eps_t_p', 't_p', 'logsnr_p', 'obj_weight_t_p', 'var_t_p', 'm_t_p')) pred_eps_p, pred_x0_p = self.ddpm_step(eps_t_p, t_p, logsnr_p, var_t_p, m_t_p, **model_kwargs) # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation" with self.ddp_model.no_sync(): # type: ignore if args.loss_type == 'eps': l2_term_p = th.square(pred_eps_p - noise) # ? weights elif args.loss_type == 'x0': # l2_term_p = th.square(pred_eps_p - p_sample_batch['eps']) # ? weights l2_term_p = th.square( pred_x0_p - p_sample_batch['eps'].detach()) # ? weights # if args.loss_weight == 'snr': # obj_weight_t_p = th.sigmoid(th.exp(logsnr_p)) else: raise NotImplementedError(args.loss_type) # p_eps_objective = th.mean(obj_weight_t_p * l2_term_p) p_eps_objective = obj_weight_t_p * l2_term_p if p_sample_batch['mode'] == 'q': ce_weight = self.ce_weight() p_eps_objective = p_eps_objective * ce_weight log_rec3d_loss_dict({ 'ce_weight': ce_weight, }) log_rec3d_loss_dict({ f"{p_sample_batch['mode']}_loss": p_eps_objective.mean(), }) if self.ddpm_model.mixed_prediction: log_rec3d_loss_dict({ 'mixing_logit': self.ddp_ddpm_model(x=None, timesteps=None, get_attr='mixing_logit').detach(),}) return { 'pred_eps_p': pred_eps_p, 'eps_t_p': eps_t_p, 'p_eps_objective': p_eps_objective, 'pred_x0_p': pred_x0_p, 'logsnr_p': logsnr_p } def ddpm_step(self, eps_t, t, logsnr, var_t, m_t, **model_kwargs): """helper function for ddpm predictions; returns predicted eps, x0 and logsnr. args notes: eps_t is x_noisy """ args = self.sde_diffusion.args pred_params = self.ddp_ddpm_model(x=eps_t, timesteps=t, **model_kwargs) # logsnr = self.sde_diffusion.log_snr(m_t, var_t) # for p only if args.pred_type in ['eps', 'v']: if args.pred_type == 'v': pred_eps = self.sde_diffusion._predict_eps_from_z_and_v( pred_params, var_t, eps_t, m_t ) # pred_x0 = self.sde_diffusion._predict_x0_from_z_and_v( # pred_params, var_t, eps_t, m_t) # ! verified else: pred_eps = pred_params # mixing normal trick if self.ddpm_model.mixed_prediction: mixing_component = self.sde_diffusion.mixing_component( eps_t, var_t, t, enabled=True) # z_t * sigma_t pred_eps = get_mixed_prediction( True, pred_eps, self.ddp_ddpm_model(x=None, timesteps=None, get_attr='mixing_logit'), mixing_component) pred_x0 = self.sde_diffusion._predict_x0_from_eps( eps_t, pred_eps, logsnr) # for VAE loss, denosied latent # eps, pred_params, logsnr) # for VAE loss, denosied latent elif args.pred_type == 'x0': # ! pred_x0_mixed = alpha * pred_x0 + (1-alpha) * z_t * alpha_t pred_x0 = pred_params # how to mix? # mixing normal trick mixing_component = self.sde_diffusion.mixing_component_x0( eps_t, var_t, t, enabled=True) # z_t * alpha_t pred_x0 = get_mixed_prediction( True, pred_x0, self.ddp_ddpm_model(x=None, timesteps=None, get_attr='mixing_logit'), mixing_component) pred_eps = self.sde_diffusion._predict_eps_from_x0( eps_t, pred_x0, logsnr) else: raise NotImplementedError(f'{args.pred_type} not implemented.') log_rec3d_loss_dict({ f'pred_x0_mean': pred_x0.mean(), f'pred_x0_std': pred_x0.std([1,2,3]).mean(0), f'pred_x0_max': pred_x0.max(), }) return pred_eps, pred_x0 def ddpm_loss(self, noise, pred_eps, last_batch): # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation" if last_batch or not self.use_ddp: l2_term = th.square(pred_eps - noise) else: with self.ddp_model.no_sync(): # type: ignore l2_term = th.square(pred_eps - noise) # ? weights return l2_term def run_step(self, batch, step='diffusion_step_rec'): if step == 'ce_ddpm_step': self.ce_ddpm_step(batch) elif step == 'p_rendering_step': self.p_rendering_step(batch) elif step == 'eps_step': self.eps_step(batch) # ! both took ddpm step self._update_ema() self._anneal_lr() self.log_step() @th.inference_mode() def _post_run_loop(self): # if self.step % self.eval_interval == 0 and self.step != 0: # if self.step % self.eval_interval == 0: # if dist_util.get_rank() == 0: # self.eval_ddpm_sample( # self.rec_model, # # self.ddpm_model # ) # ! only support single GPU inference now. # if self.sde_diffusion.args.train_vae: # self.eval_loop(self.ddp_rec_model) if self.step % self.log_interval == 0 and dist_util.get_rank() == 0: out = logger.dumpkvs() # * log to tensorboard for k, v in out.items(): self.writer.add_scalar(f'Loss/{k}', v, self.step + self.resume_step) # if self.step % self.eval_interval == 0 and self.step != 0: if self.step % self.eval_interval == 0: if dist_util.get_rank() == 0: self.eval_ddpm_sample(self.ddp_rec_model) if self.sde_diffusion.args.train_vae: self.eval_loop(self.ddp_rec_model) if self.step % self.save_interval == 0: self.save(self.mp_trainer, self.mp_trainer.model_name) self.step += 1 if self.step > self.iterations: print('reached maximum iterations, exiting') # Save the last checkpoint if it wasn't already saved. if (self.step - 1) % self.save_interval != 0: self.save(self.mp_trainer, self.mp_trainer.model_name) exit() def run_loop(self): while (not self.lr_anneal_steps or self.step + self.resume_step < self.lr_anneal_steps): # let all processes sync up before starting with a new epoch of training dist_util.synchronize() batch = next(self.data) self.run_step(batch, step='ce_ddpm_step') self._post_run_loop() # batch = next(self.data) # self.run_step(batch, step='p_rendering_step') def ce_ddpm_step(self, batch, behaviour='rec', *args, **kwargs): """ add sds grad to all ae predicted x_0 """ args = self.sde_diffusion.args assert args.train_vae requires_grad(self.rec_model, args.train_vae) requires_grad(self.ddpm_model, True) # TODO merge? self.mp_trainer.zero_grad() batch_size = batch['img'].shape[0] for i in range(0, batch_size, self.microbatch): micro = { k: v[i:i + self.microbatch].to(dist_util.dev()) if isinstance( v, th.Tensor) else v for k, v in batch.items() } last_batch = (i + self.microbatch) >= batch_size q_vae_recon_loss = th.tensor(0.0).to(dist_util.dev()) # vision_aided_loss = th.tensor(0.0).to(dist_util.dev()) # denoise_loss = th.tensor(0.0).to(dist_util.dev()) # =================================== ae part =================================== with th.cuda.amp.autocast(dtype=th.float16, enabled=self.mp_trainer.use_amp): # ! part 1: train vae with CE; ddpm fixed # ! TODO, add KL_all_list? vae.decompose with th.set_grad_enabled(args.train_vae): # vae_out = self.ddp_rec_model( # img=micro['img_to_encoder'], # c=micro['c'], # behaviour='encoder_vae', # ) # pred: (B, 3, 64, 64) # TODO, no need to render if not SSD; no need to do ViT decoder if only the latent is needed. update later # if args.train_vae: # if args.add_rendering_loss: # if args.joint_train: # with th.set_grad_enabled(args.train_vae): pred = self.ddp_rec_model( # latent=vae_out, img=micro['img_to_encoder'], c=micro['c'], ) # behaviour=self.render_latent_behaviour) # vae reconstruction loss if last_batch or not self.use_ddp: q_vae_recon_loss, loss_dict = self.loss_class( pred, micro, test_mode=False) else: with self.ddp_model.no_sync(): # type: ignore q_vae_recon_loss, loss_dict = self.loss_class( pred, micro, test_mode=False) log_rec3d_loss_dict(loss_dict) # ''' # ! calculate p/q loss; # nelbo_loss = balanced_kl * self.loss_class.opt.ce_balanced_kl + q_vae_recon_loss nelbo_loss = q_vae_recon_loss q_loss = th.mean(nelbo_loss) # st() # all_log_q = [vae_out['log_q_2Ddiffusion']] # eps = vae_out[self.latent_name] # all_log_q = [pred['log_q_2Ddiffusion']] eps = pred[self.latent_name] if not args.train_vae: eps.requires_grad_(True) # single stage diffusion # sample noise noise = th.randn( size=eps.size(), device=eps.device ) # note that this noise value is currently shared! # in case we want to train q (vae) with another batch using a different sampling scheme for times t ''' assert args.iw_sample_q in ['ll_uniform', 'll_iw'] t_q, var_t_q, m_t_q, obj_weight_t_q, _, g2_t_q = \ self.sde_diffusion.iw_quantities(args.iw_sample_q) eps_t_q = self.sde_diffusion.sample_q(eps, noise, var_t_q, m_t_q) # eps_t = th.cat([eps_t_p, eps_t_q], dim=0) # var_t = th.cat([var_t_p, var_t_q], dim=0) # t = th.cat([t_p, t_q], dim=0) # noise = th.cat([noise, noise], dim=0) # run the diffusion model if not args.train_vae: eps_t_q.requires_grad_(True) # 2*BS, 12, 16, 16 # ! For CE guidance. requires_grad(self.ddpm_model_module, False) pred_eps_q, _, _ = self.ddpm_step(eps_t_q, t_q, m_t_q, var_t_q) l2_term_q = self.ddpm_loss(noise, pred_eps_q, last_batch) # pred_eps = th.cat([pred_eps_p, pred_eps_q], dim=0) # p then q # ÇE: nelbo loss with kl balancing assert args.iw_sample_q in ['ll_uniform', 'll_iw'] # l2_term_p, l2_term_q = th.chunk(l2_term, chunks=2, dim=0) cross_entropy_per_var = obj_weight_t_q * l2_term_q cross_entropy_per_var += self.sde_diffusion.cross_entropy_const( args.sde_time_eps) all_neg_log_p = [cross_entropy_per_var ] # since only one vae group kl_all_list, kl_vals_per_group, kl_diag_list = kl_per_group_vada( all_log_q, all_neg_log_p) # return the mean of two terms # nelbo loss with kl balancing balanced_kl, kl_coeffs, kl_vals = kl_balancer(kl_all_list, kl_coeff=1.0, kl_balance=False, alpha_i=None) # st() log_rec3d_loss_dict( dict( balanced_kl=balanced_kl, l2_term_q=l2_term_q, cross_entropy_per_var=cross_entropy_per_var.mean(), all_log_q=all_log_q[0].mean(), )) ''' # ! update vae for CE # ! single stage diffusion for rec side 1: bind vae prior and diffusion prior # ! BP for CE and VAE; quit the AMP context. # if args.train_vae: # self.mp_trainer.backward(q_loss) # _ = self.mp_trainer.optimize(self.opt) # retain_graph=different_p_q_objectives( # args.iw_sample_p, # args.iw_sample_q)) log_rec3d_loss_dict( dict(q_vae_recon_loss=q_vae_recon_loss, # all_log_q=all_log_q[0].mean(), )) # ! adding p loss; enable ddpm gradient # self.mp_trainer.zero_grad() # requires_grad(self.rec_model_module, # False) # could be removed since eps_t_p.detach() with th.cuda.amp.autocast(dtype=th.float16, enabled=self.mp_trainer.use_amp): # first get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae) t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \ self.sde_diffusion.iw_quantities(args.iw_sample_p) eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p, m_t_p) eps_t_p = eps_t_p.detach( ) # .requires_grad_(True) # ! update ddpm not rec module pred_eps_p, _, = self.ddpm_step(eps_t_p, t_p, m_t_p, var_t_p) l2_term_p = self.ddpm_loss(noise, pred_eps_p, last_batch) p_loss = th.mean(obj_weight_t_p * l2_term_p) # ! update ddpm self.mp_trainer.backward(p_loss + q_loss) # just backward for p_loss _ = self.mp_trainer.optimize(self.opt) # requires_grad(self.rec_model_module, True) log_rec3d_loss_dict( dict( p_loss=p_loss, mixing_logit=self.ddp_ddpm_model( x=None, timesteps=None, get_attr='mixing_logit').detach(), )) # TODO, merge visualization with original AE # =================================== denoised AE log part =================================== # ! todo, wrap in a single function if dist_util.get_rank() == 0 and self.step % 500 == 0: with th.no_grad(): if not args.train_vae: vae_out.pop('posterior') # for calculating kl loss vae_out_for_pred = { k: v[0:1].to(dist_util.dev()) if isinstance( v, th.Tensor) else v for k, v in vae_out.items() } pred = self.ddp_rec_model( latent=vae_out_for_pred, c=micro['c'][0:1], behaviour=self.render_latent_behaviour) assert isinstance(pred, dict) assert pred is not None gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - gt_depth.min()) if 'image_depth' in pred: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) else: pred_depth = th.zeros_like(gt_depth) pred_img = pred['image_raw'] gt_img = micro['img'] if 'image_sr' in pred: if pred['image_sr'].shape[-1] == 512: pred_img = th.cat( [self.pool_512(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_512(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_512(pred_depth) gt_depth = self.pool_512(gt_depth) elif pred['image_sr'].shape[-1] == 256: pred_img = th.cat( [self.pool_256(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_256(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_256(pred_depth) gt_depth = self.pool_256(gt_depth) else: pred_img = th.cat( [self.pool_128(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_128(micro['img']), micro['img_sr']], dim=-1) gt_depth = self.pool_128(gt_depth) pred_depth = self.pool_128(pred_depth) else: gt_img = self.pool_64(gt_img) gt_depth = self.pool_64(gt_depth) gt_vis = th.cat( [ gt_img, # micro['img'], gt_depth.repeat_interleave(3, dim=1) ], dim=-1)[0:1] # TODO, fail to load depth. range [0, 1] pred_vis = th.cat([ pred_img[0:1], pred_depth[0:1].repeat_interleave(3, dim=1) ], dim=-1) # B, 3, H, W vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute( 1, 2, 0).cpu() # ! pred in range[-1, 1] # vis_grid = torchvision.utils.make_grid(vis) # HWC vis = vis.numpy() * 127.5 + 127.5 vis = vis.clip(0, 255).astype(np.uint8) Image.fromarray(vis).save( # f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t[0].item()}_{behaviour}.jpg' f'{logger.get_dir()}/{self.step+self.resume_step}_{behaviour}.jpg' ) print( 'log denoised vis to: ', f'{logger.get_dir()}/{self.step+self.resume_step}_{behaviour}.jpg' ) th.cuda.empty_cache() def eps_step(self, batch, behaviour='rec', *args, **kwargs): """ add sds grad to all ae predicted x_0 """ args = self.sde_diffusion.args requires_grad(self.ddpm_model_module, True) requires_grad(self.rec_model_module, False) # TODO? # if args.train_vae: # for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters( # type: ignore # ): # type: ignore # param.requires_grad_( # False # ) # ! disable triplane_decoder grad in each iteration indepenently; self.mp_trainer.zero_grad() # assert args.train_vae batch_size = batch['img'].shape[0] for i in range(0, batch_size, self.microbatch): micro = { k: v[i:i + self.microbatch].to(dist_util.dev()) if isinstance( v, th.Tensor) else v for k, v in batch.items() } last_batch = (i + self.microbatch) >= batch_size # =================================== ae part =================================== with th.cuda.amp.autocast(dtype=th.float16, enabled=self.mp_trainer.use_amp): # and args.train_vae): # ! part 1: train vae with CE; ddpm fixed # ! TODO, add KL_all_list? vae.decompose with th.set_grad_enabled(args.train_vae): vae_out = self.ddp_rec_model( img=micro['img_to_encoder'], c=micro['c'], behaviour='encoder_vae', ) # pred: (B, 3, 64, 64) eps = vae_out[self.latent_name] # sample noise noise = th.randn( size=eps.size(), device=eps.device ) # note that this noise value is currently shared! # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae) t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \ self.sde_diffusion.iw_quantities(args.iw_sample_p) eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p, m_t_p) logsnr_p = self.sde_diffusion.log_snr(m_t_p, var_t_p) # for p only pred_eps_p, pred_x0_p, logsnr_p = self.ddpm_step( eps_t_p, t_p, m_t_p, var_t_p) # ! batchify for mixing_component # mixing normal trick mixing_component = self.sde_diffusion.mixing_component( eps_t_p, var_t_p, t_p, enabled=True) # TODO, which should I use? pred_eps_p = get_mixed_prediction( True, pred_eps_p, self.ddp_ddpm_model(x=None, timesteps=None, get_attr='mixing_logit'), mixing_component) # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation" if last_batch or not self.use_ddp: l2_term_p = th.square(pred_eps_p - noise) else: with self.ddp_ddpm_model.no_sync(): # type: ignore l2_term_p = th.square(pred_eps_p - noise) # ? weights p_eps_objective = th.mean( obj_weight_t_p * l2_term_p) * self.loss_class.opt.p_eps_lambda log_rec3d_loss_dict( dict(mixing_logit=self.ddp_ddpm_model( x=None, timesteps=None, get_attr='mixing_logit').detach(), )) # ===================================================================== # ! single stage diffusion for rec side 2: generative feature # if args.p_rendering_loss: # target = micro # pred = self.ddp_rec_model( # # latent=vae_out, # latent={ # **vae_out, self.latent_name: pred_x0_p, # 'latent_name': self.latent_name # }, # c=micro['c'], # behaviour=self.render_latent_behaviour) # # vae reconstruction loss # if last_batch or not self.use_ddp: # p_vae_recon_loss, _ = self.loss_class(pred, # target, # test_mode=False) # else: # with self.ddp_model.no_sync(): # type: ignore # p_vae_recon_loss, _ = self.loss_class( # pred, target, test_mode=False) # log_rec3d_loss_dict( # dict(p_vae_recon_loss=p_vae_recon_loss, )) # p_loss = p_eps_objective + p_vae_recon_loss # else: p_loss = p_eps_objective log_rec3d_loss_dict( dict(p_loss=p_loss, p_eps_objective=p_eps_objective)) # ! to arrange: update vae params self.mp_trainer.backward(p_loss) # update ddpm accordingly _ = self.mp_trainer.optimize( self.opt) # TODO, update two groups of parameters # TODO, merge visualization with original AE # ! todo, merge required # =================================== denoised AE log part =================================== if dist_util.get_rank( ) == 0 and self.step % 500 == 0 and behaviour != 'diff': with th.no_grad(): vae_out.pop('posterior') # for calculating kl loss vae_out_for_pred = { k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v for k, v in vae_out.items() } pred = self.ddp_rec_model( latent=vae_out_for_pred, c=micro['c'][0:1], behaviour=self.render_latent_behaviour) assert isinstance(pred, dict) gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - gt_depth.min()) if 'image_depth' in pred: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) else: pred_depth = th.zeros_like(gt_depth) pred_img = pred['image_raw'] gt_img = micro['img'] if 'image_sr' in pred: if pred['image_sr'].shape[-1] == 512: pred_img = th.cat( [self.pool_512(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_512(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_512(pred_depth) gt_depth = self.pool_512(gt_depth) elif pred['image_sr'].shape[-1] == 256: pred_img = th.cat( [self.pool_256(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_256(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_256(pred_depth) gt_depth = self.pool_256(gt_depth) else: pred_img = th.cat( [self.pool_128(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_128(micro['img']), micro['img_sr']], dim=-1) gt_depth = self.pool_128(gt_depth) pred_depth = self.pool_128(pred_depth) else: gt_img = self.pool_64(gt_img) gt_depth = self.pool_64(gt_depth) gt_vis = th.cat( [ gt_img, micro['img'], micro['img'], gt_depth.repeat_interleave(3, dim=1) ], dim=-1)[0:1] # TODO, fail to load depth. range [0, 1] # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L noised_ae_pred = self.ddp_rec_model( img=None, c=micro['c'][0:1], latent=eps_t_p[0:1] * self. triplane_scaling_divider, # TODO, how to define the scale automatically behaviour=self.render_latent_behaviour) pred_x0 = self.sde_diffusion._predict_x0_from_eps( eps_t_p, pred_eps_p, logsnr_p) # for VAE loss, denosied latent # pred_xstart_3D denoised_ae_pred = self.ddp_rec_model( img=None, c=micro['c'][0:1], latent=pred_x0[0:1] * self. triplane_scaling_divider, # TODO, how to define the scale automatically? behaviour=self.render_latent_behaviour) pred_vis = th.cat([ pred_img[0:1], noised_ae_pred['image_raw'][0:1], denoised_ae_pred['image_raw'][0:1], pred_depth[0:1].repeat_interleave(3, dim=1) ], dim=-1) # B, 3, H, W vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute( 1, 2, 0).cpu() # ! pred in range[-1, 1] # vis_grid = torchvision.utils.make_grid(vis) # HWC vis = vis.numpy() * 127.5 + 127.5 vis = vis.clip(0, 255).astype(np.uint8) Image.fromarray(vis).save( f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg' ) print( 'log denoised vis to: ', f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg' ) del vis, pred_vis, pred_x0, pred_eps_p, micro, vae_out th.cuda.empty_cache() def p_rendering_step(self, batch, behaviour='rec', *args, **kwargs): """ add sds grad to all ae predicted x_0 """ args = self.sde_diffusion.args requires_grad(self.ddpm_model, True) requires_grad(self.rec_model, args.train_vae) # TODO? # if args.train_vae: # for param in self.ddp_rec_model.module.decoder.triplane_decoder.parameters( # type: ignore # ): # type: ignore # param.requires_grad_( # False # ) # ! disable triplane_decoder grad in each iteration indepenently; self.mp_trainer.zero_grad() assert args.train_vae batch_size = batch['img'].shape[0] for i in range(0, batch_size, self.microbatch): micro = { k: v[i:i + self.microbatch].to(dist_util.dev()) if isinstance( v, th.Tensor) else v for k, v in batch.items() } last_batch = (i + self.microbatch) >= batch_size # =================================== ae part =================================== with th.cuda.amp.autocast(dtype=th.float16, enabled=self.mp_trainer.use_amp): # and args.train_vae): # ! part 1: train vae with CE; ddpm fixed # ! TODO, add KL_all_list? vae.decompose with th.set_grad_enabled(args.train_vae): vae_out = self.ddp_rec_model( img=micro['img_to_encoder'], c=micro['c'], behaviour='encoder_vae', ) # pred: (B, 3, 64, 64) eps = vae_out[self.latent_name] # sample noise noise = th.randn( size=eps.size(), device=eps.device ) # note that this noise value is currently shared! # get diffusion quantities for p (sgm prior) sampling scheme and reweighting for q (vae) t_p, var_t_p, m_t_p, obj_weight_t_p, obj_weight_t_q, g2_t_p = \ self.sde_diffusion.iw_quantities(args.iw_sample_p) eps_t_p = self.sde_diffusion.sample_q(eps, noise, var_t_p, m_t_p) logsnr_p = self.sde_diffusion.log_snr(m_t_p, var_t_p) # for p only # pred_eps_p, pred_x0_p, logsnr_p = self.ddpm_step( pred_eps_p, pred_x0_p = self.ddpm_step(eps_t_p, t_p, logsnr_p, var_t_p) # eps_t_p, t_p, m_t_p, var_t_p) # ! batchify for mixing_component # mixing normal trick # mixing_component = self.sde_diffusion.mixing_component( # eps_t_p, var_t_p, t_p, # enabled=True) # TODO, which should I use? # pred_eps_p = get_mixed_prediction( # True, pred_eps_p, # self.ddp_ddpm_model(x=None, # timesteps=None, # get_attr='mixing_logit'), # mixing_component) # ! eps loss equivalent to snr weighting of x0 loss, see "progressive distillation" if last_batch or not self.use_ddp: l2_term_p = th.square(pred_eps_p - noise) else: with self.ddp_model.no_sync(): # type: ignore l2_term_p = th.square(pred_eps_p - noise) # ? weights p_eps_objective = th.mean(obj_weight_t_p * l2_term_p) # st() log_rec3d_loss_dict( dict(mixing_logit=self.ddp_ddpm_model( x=None, timesteps=None, get_attr='mixing_logit').detach(), )) # ===================================================================== # ! single stage diffusion for rec side 2: generative feature if args.p_rendering_loss: target = micro pred = self.ddp_rec_model( # re-render latent={ **vae_out, self.latent_name: pred_x0_p, 'latent_name': self.latent_name }, c=micro['c'], behaviour=self.render_latent_behaviour) # vae reconstruction loss if last_batch or not self.use_ddp: pred[self.latent_name] = vae_out[self.latent_name] pred[ 'latent_name'] = self.latent_name # just for stats p_vae_recon_loss, rec_loss_dict = self.loss_class( pred, target, test_mode=False) else: with self.ddp_model.no_sync(): # type: ignore p_vae_recon_loss, rec_loss_dict = self.loss_class( pred, target, test_mode=False) log_rec3d_loss_dict( dict(p_vae_recon_loss=p_vae_recon_loss, )) for key in rec_loss_dict.keys(): if 'latent' in key: log_rec3d_loss_dict({key: rec_loss_dict[key]}) p_loss = p_eps_objective + p_vae_recon_loss else: p_loss = p_eps_objective log_rec3d_loss_dict( dict(p_loss=p_loss, p_eps_objective=p_eps_objective)) # ! to arrange: update vae params self.mp_trainer.backward(p_loss) # update ddpm accordingly _ = self.mp_trainer.optimize( self.opt) # TODO, update two groups of parameters # TODO, merge visualization with original AE # ! todo, merge required # =================================== denoised AE log part =================================== if dist_util.get_rank( ) == 0 and self.step % 500 == 0 and behaviour != 'diff': with th.no_grad(): vae_out.pop('posterior') # for calculating kl loss vae_out_for_pred = { k: v[0:1].to(dist_util.dev()) if isinstance(v, th.Tensor) else v for k, v in vae_out.items() } pred = self.ddp_rec_model( latent=vae_out_for_pred, c=micro['c'][0:1], behaviour=self.render_latent_behaviour) assert isinstance(pred, dict) gt_depth = micro['depth'] if gt_depth.ndim == 3: gt_depth = gt_depth.unsqueeze(1) gt_depth = (gt_depth - gt_depth.min()) / (gt_depth.max() - gt_depth.min()) if 'image_depth' in pred: pred_depth = pred['image_depth'] pred_depth = (pred_depth - pred_depth.min()) / ( pred_depth.max() - pred_depth.min()) else: pred_depth = th.zeros_like(gt_depth) pred_img = pred['image_raw'] gt_img = micro['img'] if 'image_sr' in pred: if pred['image_sr'].shape[-1] == 512: pred_img = th.cat( [self.pool_512(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_512(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_512(pred_depth) gt_depth = self.pool_512(gt_depth) elif pred['image_sr'].shape[-1] == 256: pred_img = th.cat( [self.pool_256(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_256(micro['img']), micro['img_sr']], dim=-1) pred_depth = self.pool_256(pred_depth) gt_depth = self.pool_256(gt_depth) else: pred_img = th.cat( [self.pool_128(pred_img), pred['image_sr']], dim=-1) gt_img = th.cat( [self.pool_128(micro['img']), micro['img_sr']], dim=-1) gt_depth = self.pool_128(gt_depth) pred_depth = self.pool_128(pred_depth) else: gt_img = self.pool_64(gt_img) gt_depth = self.pool_64(gt_depth) gt_vis = th.cat( [ gt_img, micro['img'], micro['img'], gt_depth.repeat_interleave(3, dim=1) ], dim=-1)[0:1] # TODO, fail to load depth. range [0, 1] # eps_t_p_3D = eps_t_p.reshape(batch_size, eps_t_p.shape[1]//3, 3, -1) # B C 3 L noised_ae_pred = self.ddp_rec_model( img=None, c=micro['c'][0:1], latent=eps_t_p[0:1] * self. triplane_scaling_divider, # TODO, how to define the scale automatically behaviour=self.render_latent_behaviour) pred_x0 = self.sde_diffusion._predict_x0_from_eps( eps_t_p, pred_eps_p, logsnr_p) # for VAE loss, denosied latent # pred_xstart_3D denoised_ae_pred = self.ddp_rec_model( img=None, c=micro['c'][0:1], latent=pred_x0[0:1] * self. triplane_scaling_divider, # TODO, how to define the scale automatically? behaviour=self.render_latent_behaviour) pred_vis = th.cat([ pred_img[0:1], noised_ae_pred['image_raw'][0:1], denoised_ae_pred['image_raw'][0:1], pred_depth[0:1].repeat_interleave(3, dim=1) ], dim=-1) # B, 3, H, W vis = th.cat([gt_vis, pred_vis], dim=-2)[0].permute( 1, 2, 0).cpu() # ! pred in range[-1, 1] # vis_grid = torchvision.utils.make_grid(vis) # HWC vis = vis.numpy() * 127.5 + 127.5 vis = vis.clip(0, 255).astype(np.uint8) Image.fromarray(vis).save( f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg' ) print( 'log denoised vis to: ', f'{logger.get_dir()}/{self.step+self.resume_step}denoised_{t_p[0].item():3}_{behaviour}.jpg' ) del vis, pred_vis, pred_x0, pred_eps_p, micro, vae_out th.cuda.empty_cache() class TrainLoop3DDiffusionLSGMJointnoD_ponly(TrainLoop3DDiffusionLSGMJointnoD): def __init__(self, *, rec_model, denoise_model, sde_diffusion, loss_class, data, eval_data, batch_size, microbatch, lr, ema_rate, log_interval, eval_interval, save_interval, resume_checkpoint, use_fp16=False, fp16_scale_growth=0.001, weight_decay=0, lr_anneal_steps=0, iterations=10001, triplane_scaling_divider=1, use_amp=False, diffusion_input_size=224, **kwargs): super().__init__(rec_model=rec_model, denoise_model=denoise_model, sde_diffusion=sde_diffusion, loss_class=loss_class, data=data, eval_data=eval_data, batch_size=batch_size, microbatch=microbatch, lr=lr, ema_rate=ema_rate, log_interval=log_interval, eval_interval=eval_interval, save_interval=save_interval, resume_checkpoint=resume_checkpoint, use_fp16=use_fp16, fp16_scale_growth=fp16_scale_growth, weight_decay=weight_decay, lr_anneal_steps=lr_anneal_steps, iterations=iterations, triplane_scaling_divider=triplane_scaling_divider, use_amp=use_amp, diffusion_input_size=diffusion_input_size, **kwargs) def run_loop(self): while (not self.lr_anneal_steps or self.step + self.resume_step < self.lr_anneal_steps): self._post_run_loop() # let all processes sync up before starting with a new epoch of training dist_util.synchronize() # batch = next(self.data) # self.run_step(batch, step='ce_ddpm_step') batch = next(self.data) self.run_step(batch, step='p_rendering_step') # self.run_step(batch, step='eps_step')