450 lines
21 KiB
Python
450 lines
21 KiB
Python
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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""" to train hierarchical VAE model with 2 prior
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one for style latent, one for latent pts,
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based on trainers/train_prior.py
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"""
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import os
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import time
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from PIL import Image
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import gc
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import functools
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import psutil
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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import torchvision
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import numpy as np
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from loguru import logger
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import torch.distributed as dist
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from torch import optim
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from utils.ema import EMA
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from utils.model_helper import import_model, loss_fn
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from utils.vis_helper import visualize_point_clouds_3d
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from utils.eval_helper import compute_NLL_metric
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from utils import model_helper, exp_helper, data_helper
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from utils.data_helper import normalize_point_clouds
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## from utils.diffusion_discretized import DiffusionDiscretized
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from utils.diffusion_pvd import DiffusionDiscretized
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from utils.diffusion_continuous import make_diffusion, DiffusionBase
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from utils.checker import *
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from utils import utils
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from matplotlib import pyplot as plt
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import third_party.pvcnn.functional as pvcnn_fn
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from timeit import default_timer as timer
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from torch.optim import Adam as FusedAdam
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from torch.cuda.amp import autocast, GradScaler
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from trainers.train_prior import Trainer as PriorTrainer
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from trainers.train_prior import validate_inspect # import Trainer as PriorTrainer
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quiet = int(os.environ.get('quiet', 0))
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VIS_LATENT_PTS = 0
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@torch.no_grad()
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def generate_samples_vada_2prior(shape, dae, diffusion, vae, num_samples, enable_autocast,
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ode_eps=0.00001, ode_solver_tol=1e-5, # None,
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ode_sample=False, prior_var=1.0, temp=1.0, vae_temp=1.0, noise=None, need_denoise=False,
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ddim_step=0, clip_feat=None, cls_emb=None, ddim_skip_type='uniform', ddim_kappa=1.0):
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output = {}
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#kwargs = {}
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# if cls_emb is not None:
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# kwargs['cls_emb'] = cls_emb
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if ode_sample == 1:
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assert isinstance(
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diffusion, DiffusionBase), 'ODE-based sampling requires cont. diffusion!'
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assert ode_eps is not None, 'ODE-based sampling requires integration cutoff ode_eps!'
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assert ode_solver_tol is not None, 'ODE-based sampling requires ode solver tolerance!'
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start = timer()
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condition_input = None
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eps_list = []
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for i in range(len(dae)):
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assert(cls_emb is None), f' not support yet'
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eps, nfe, time_ode_solve = diffusion.sample_model_ode(
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dae[i], num_samples, shape[i], ode_eps, ode_solver_tol, enable_autocast, temp, noise,
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condition_input=condition_input, clip_feat=clip_feat,
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)
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condition_input = eps
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eps_list.append(eps)
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output['sampled_eps'] = eps
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eps = vae.compose_eps(eps_list) # torch.cat(eps, dim=1)
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elif ode_sample == 0:
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assert isinstance(
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diffusion, DiffusionDiscretized), 'Regular sampling requires disc. diffusion!'
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assert noise is None, 'Noise is not used in ancestral sampling.'
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nfe = diffusion._diffusion_steps
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time_ode_solve = 999.999 # Yeah I know...
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start = timer()
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condition_input = None if cls_emb is None else cls_emb
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all_eps = []
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for i in range(len(dae)):
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if ddim_step > 0:
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assert(cls_emb is None), f'not support yet'
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eps, eps_list = diffusion.run_ddim(dae[i],
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num_samples, shape[i], temp, enable_autocast,
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is_image=False, prior_var=prior_var, ddim_step=ddim_step,
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condition_input=condition_input, clip_feat=clip_feat,
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skip_type=ddim_skip_type, kappa=ddim_kappa)
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else:
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eps, eps_list = diffusion.run_denoising_diffusion(dae[i],
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num_samples, shape[i], temp, enable_autocast,
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is_image=False, prior_var=prior_var,
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condition_input=condition_input, clip_feat=clip_feat,
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)
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condition_input = eps
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if cls_emb is not None:
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condition_input = torch.cat([condition_input,
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cls_emb.unsqueeze(-1).unsqueeze(-1)], dim=1)
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if i == 0:
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condition_input = vae.global2style(condition_input)
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# exit()
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all_eps.append(eps)
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output['sampled_eps'] = eps
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eps = vae.compose_eps(all_eps)
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output['eps_list'] = eps_list
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output['print/sample_mean_global'] = eps.view(
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num_samples, -1).mean(-1).mean()
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output['print/sample_var_global'] = eps.view(
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num_samples, -1).var(-1).mean()
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decomposed_eps = vae.decompose_eps(eps)
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image = vae.sample(num_samples=num_samples,
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decomposed_eps=decomposed_eps, cls_emb=cls_emb)
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end = timer()
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sampling_time = end - start
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# average over GPUs
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nfe_torch = torch.tensor(nfe * 1.0, device='cuda')
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sampling_time_torch = torch.tensor(sampling_time * 1.0, device='cuda')
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time_ode_solve_torch = torch.tensor(time_ode_solve * 1.0, device='cuda')
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return image, nfe_torch, time_ode_solve_torch, sampling_time_torch, output
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class Trainer(PriorTrainer):
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is_diffusion = 0
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def __init__(self, cfg, args):
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"""
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Args:
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cfg: training config
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args: used for distributed training
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"""
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super().__init__(cfg, args)
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self.fun_generate_samples_vada = functools.partial(
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generate_samples_vada_2prior, ode_eps=cfg.sde.ode_eps,
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ddim_skip_type=cfg.sde.ddim_skip_type,
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ddim_kappa=cfg.sde.ddim_kappa)
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def compute_loss_vae(self, tr_pts, global_step, **kwargs):
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""" compute forward for VAE model, used in global-only prior training
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Input:
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tr_pts: points
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global_step: int
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Returns:
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output dict including entry:
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'eps': z ~ posterior
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'q_loss': 0 if not train vae else the KL+rec
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'x_0_pred': global points if not train vae
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'x_0_target': target points
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"""
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vae = self.model
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dae = self.dae
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args = self.cfg.sde
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distributed = args.distributed
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vae_sn_calculator = self.vae_sn_calculator
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num_total_iter = self.num_total_iter
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## diffusion = self.diffusion_cont if self.cfg.sde.ode_sample else self.diffusion_disc
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if self.cfg.sde.ode_sample == 1:
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diffusion = self.diffusion_cont
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elif self.cfg.sde.ode_sample == 0:
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diffusion = self.diffusion_disc
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elif self.cfg.sde.ode_sample == 2:
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raise NotImplementedError
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# diffusion = [self.diffusion_cont, self.diffusion_disc]
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B = tr_pts.size(0)
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with torch.set_grad_enabled(args.train_vae):
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with autocast(enabled=args.autocast_train):
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# posterior and likelihood
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if not args.train_vae:
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output = {}
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all_eps, all_log_q, latent_list = vae.encode(tr_pts)
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x_0_pred = x_0_target = tr_pts
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vae_recon_loss = 0
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def make_4d(x): return x.unsqueeze(-1).unsqueeze(-1) if \
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len(x.shape) == 2 else x.unsqueeze(-1)
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eps = make_4d(all_eps)
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output.update({'eps': eps, 'q_loss': torch.zeros(1),
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'x_0_pred': tr_pts, 'x_0_target': tr_pts,
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'x_0': tr_pts, 'final_pred': tr_pts})
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else:
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raise NotImplementedError
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return output
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# ------------------------------------------- #
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# training fun #
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# ------------------------------------------- #
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def train_iter(self, data, *args, **kwargs):
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""" forward one iteration; and step optimizer
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Args:
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data: (dict) tr_points shape: (B,N,3)
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see get_loss in models/shapelatent_diffusion.py
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"""
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# some variables
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input_dim = self.cfg.ddpm.input_dim
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loss_type = self.cfg.ddpm.loss_type
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vae = self.model
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dae = self.dae
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dae.train()
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diffusion = self.diffusion_cont if self.cfg.sde.ode_sample else self.diffusion_disc
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if self.cfg.sde.ode_sample == 1:
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diffusion = self.diffusion_cont
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elif self.cfg.sde.ode_sample == 0:
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diffusion = self.diffusion_disc
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elif self.cfg.sde.ode_sample == 2:
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raise NotImplementedError # not support training with different solver
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## diffusion = [self.diffusion_cont, self.diffusion_disc]
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dae_optimizer = self.dae_optimizer
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vae_optimizer = self.vae_optimizer
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args = self.cfg.sde
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device = torch.device(self.device_str)
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num_total_iter = self.num_total_iter
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distributed = self.args.distributed
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dae_sn_calculator = self.dae_sn_calculator
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vae_sn_calculator = self.vae_sn_calculator
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grad_scalar = self.grad_scalar
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global_step = step = kwargs.get('step', None)
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no_update = kwargs.get('no_update', False)
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# update_lr
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warmup_iters = len(self.train_loader) * args.warmup_epochs
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utils.update_lr(args, global_step, warmup_iters,
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dae_optimizer, vae_optimizer)
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# input
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tr_pts = data['tr_points'].to(device) # (B, Npoints, 3)
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inputs = data['input_pts'].to(
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device) if 'input_pts' in data else None # the noisy points
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tr_img = data['tr_img'].to(device) if 'tr_img' in data else None
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model_kwargs = {}
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if self.cfg.data.cond_on_cat:
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class_label_int = data['cate_idx'].view(-1) # .to(device)
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nclass = self.cfg.data.nclass
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class_label = torch.nn.functional.one_hot(class_label_int, nclass)
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model_kwargs['class_label'] = class_label.float().to(device)
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B = batch_size = tr_pts.size(0)
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if tr_img is not None:
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# tr_img: B,nimg,3,H,W
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# logger.info('image: {}', tr_img.shape)
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nimg = tr_img.shape[1]
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tr_img = tr_img.view(B*nimg, *tr_img.shape[2:])
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clip_feat = self.clip_model.encode_image(
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tr_img).view(B, nimg, -1).mean(1).float()
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else:
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clip_feat = None
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# optimize vae params
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vae_optimizer.zero_grad()
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output = self.compute_loss_vae(
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tr_pts, global_step, inputs=inputs, **model_kwargs)
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# the interface between VAE and DAE is eps.
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eps = output['eps'].detach() # 4d: B,D,-1,1
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CHECK4D(eps)
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dae_kwarg = {}
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if self.cfg.data.cond_on_cat:
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dae_kwarg['condition_input'] = output['cls_emb']
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# train prior
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if args.train_dae:
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dae_optimizer.zero_grad()
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with autocast(enabled=args.autocast_train):
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# get diffusion quantities for p sampling scheme and reweighting for q
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t_p, var_t_p, m_t_p, obj_weight_t_p, _, g2_t_p = \
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diffusion.iw_quantities(B, args.time_eps,
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args.iw_sample_p, args.iw_subvp_like_vp_sde)
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# logger.info('t_p: {}, var: {}, m_t: {}', t_p[0], var_t_p[0], m_t_p[0])
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decomposed_eps = self.vae.decompose_eps(eps)
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output['vis/eps'] = decomposed_eps[1].view(
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-1, self.dae.num_points, self.dae.num_classes)[:, :, :3]
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p_loss_list = []
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for latent_id, eps in enumerate(decomposed_eps):
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noise_p = torch.randn(size=eps.size(), device=device)
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eps_t_p = diffusion.sample_q(eps, noise_p, var_t_p, m_t_p)
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# run the score model
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eps_t_p.requires_grad_(True)
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mixing_component = diffusion.mixing_component(
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eps_t_p, var_t_p, t_p, enabled=args.mixed_prediction)
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if latent_id == 0:
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pred_params_p = dae[latent_id](
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eps_t_p, t_p, x0=eps, clip_feat=clip_feat, **dae_kwarg)
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else:
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condition_input = decomposed_eps[0] if not self.cfg.data.cond_on_cat else \
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torch.cat(
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[decomposed_eps[0], output['cls_emb'].unsqueeze(-1).unsqueeze(-1)], dim=1)
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condition_input = self.model.global2style(
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condition_input)
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pred_params_p = dae[latent_id](eps_t_p, t_p, x0=eps,
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condition_input=condition_input, clip_feat=clip_feat)
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pred_eps_t0 = (eps_t_p - torch.sqrt(var_t_p)
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* pred_params_p) / m_t_p
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params = utils.get_mixed_prediction(args.mixed_prediction,
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pred_params_p, dae[latent_id].mixing_logit, mixing_component)
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if self.cfg.latent_pts.pvd_mse_loss:
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p_loss = F.mse_loss(
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params.contiguous().view(B, -1), noise_p.view(B, -1),
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reduction='mean')
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else:
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l2_term_p = torch.square(params - noise_p)
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p_objective = torch.sum(
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obj_weight_t_p * l2_term_p, dim=[1, 2, 3])
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regularization_p, dae_norm_loss, dae_bn_loss, dae_wdn_coeff, \
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jac_reg_loss, kin_reg_loss = utils.dae_regularization(
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args, dae_sn_calculator, diffusion, dae, step, t_p,
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pred_params_p, eps_t_p, var_t_p, m_t_p, g2_t_p)
|
||
|
|
reg_mlogit = ((torch.sum(torch.sigmoid(dae.mixing_logit)) -
|
||
|
|
args.regularize_mlogit_margin)**2) * args.regularize_mlogit \
|
||
|
|
if args.regularize_mlogit else 0
|
||
|
|
p_loss = torch.mean(p_objective) + \
|
||
|
|
regularization_p + reg_mlogit
|
||
|
|
if self.writer is not None:
|
||
|
|
self.writer.avg_meter(
|
||
|
|
'train/p_loss_%d' % latent_id, p_loss.detach().item())
|
||
|
|
p_loss_list.append(p_loss)
|
||
|
|
p_loss = sum(p_loss_list) # torch.cat(p_loss_list, dim=0).sum()
|
||
|
|
loss = p_loss
|
||
|
|
# update dae parameters
|
||
|
|
grad_scalar.scale(p_loss).backward()
|
||
|
|
utils.average_gradients(dae.parameters(), distributed)
|
||
|
|
if args.grad_clip_max_norm > 0.: # apply gradient clipping
|
||
|
|
grad_scalar.unscale_(dae_optimizer)
|
||
|
|
torch.nn.utils.clip_grad_norm_(dae.parameters(),
|
||
|
|
max_norm=args.grad_clip_max_norm)
|
||
|
|
grad_scalar.step(dae_optimizer)
|
||
|
|
|
||
|
|
# update grade scalar
|
||
|
|
grad_scalar.update()
|
||
|
|
|
||
|
|
if args.bound_mlogit:
|
||
|
|
dae.mixing_logit.data.clamp_(max=args.bound_mlogit_value)
|
||
|
|
# Bookkeeping!
|
||
|
|
writer = self.writer
|
||
|
|
if writer is not None:
|
||
|
|
writer.avg_meter('train/lr_dae', dae_optimizer.state_dict()[
|
||
|
|
'param_groups'][0]['lr'], global_step)
|
||
|
|
writer.avg_meter('train/lr_vae', vae_optimizer.state_dict()[
|
||
|
|
'param_groups'][0]['lr'], global_step)
|
||
|
|
if self.cfg.latent_pts.pvd_mse_loss:
|
||
|
|
writer.avg_meter(
|
||
|
|
'train/p_loss', p_loss.item(), global_step)
|
||
|
|
if args.mixed_prediction and global_step % 500 == 0:
|
||
|
|
for i in range(len(dae)):
|
||
|
|
m = torch.sigmoid(dae[i].mixing_logit)
|
||
|
|
if not torch.isnan(m).any():
|
||
|
|
writer.add_histogram(
|
||
|
|
'mixing_prob_%d' % i, m.detach().cpu().numpy(), global_step)
|
||
|
|
|
||
|
|
# no other loss
|
||
|
|
else:
|
||
|
|
writer.avg_meter(
|
||
|
|
'train/p_loss', (p_loss - regularization_p).item(), global_step)
|
||
|
|
if torch.is_tensor(regularization_p):
|
||
|
|
writer.avg_meter(
|
||
|
|
'train/reg_p', regularization_p.item(), global_step)
|
||
|
|
if args.regularize_mlogit:
|
||
|
|
writer.avg_meter(
|
||
|
|
'train/m_logit', reg_mlogit / args.regularize_mlogit, global_step)
|
||
|
|
if args.mixed_prediction:
|
||
|
|
writer.avg_meter(
|
||
|
|
'train/m_logit_sum', torch.sum(torch.sigmoid(dae.mixing_logit)).detach().cpu(), global_step)
|
||
|
|
if (global_step) % 500 == 0:
|
||
|
|
writer.add_scalar(
|
||
|
|
'train/norm_loss_dae', dae_norm_loss, global_step)
|
||
|
|
writer.add_scalar('train/bn_loss_dae',
|
||
|
|
dae_bn_loss, global_step)
|
||
|
|
writer.add_scalar(
|
||
|
|
'train/norm_coeff_dae', dae_wdn_coeff, global_step)
|
||
|
|
if args.mixed_prediction:
|
||
|
|
m = torch.sigmoid(dae.mixing_logit)
|
||
|
|
if not torch.isnan(m).any():
|
||
|
|
writer.add_histogram(
|
||
|
|
'mixing_prob', m.detach().cpu().numpy(), global_step)
|
||
|
|
|
||
|
|
# write stats
|
||
|
|
if self.writer is not None:
|
||
|
|
for k, v in output.items():
|
||
|
|
if 'print/' in k and step is not None:
|
||
|
|
self.writer.avg_meter(k.split('print/')[-1],
|
||
|
|
v.mean().item() if torch.is_tensor(v) else v,
|
||
|
|
step=step)
|
||
|
|
res = output
|
||
|
|
output_dict = {
|
||
|
|
'loss': loss.detach().cpu().item(),
|
||
|
|
'x_0_pred': res['x_0_pred'].detach().cpu(), # perturbed data
|
||
|
|
'x_0': res['x_0'].detach().cpu(),
|
||
|
|
# B.B,3
|
||
|
|
'x_t': res['final_pred'].detach().view(batch_size, -1, res['x_0'].shape[-1]),
|
||
|
|
't': res.get('t', None)
|
||
|
|
}
|
||
|
|
|
||
|
|
for k, v in output.items():
|
||
|
|
if 'vis/' in k:
|
||
|
|
output_dict[k] = v
|
||
|
|
return output_dict
|
||
|
|
# --------------------------------------------- #
|
||
|
|
# visulization function and sampling function #
|
||
|
|
# --------------------------------------------- #
|
||
|
|
|
||
|
|
def build_prior(self):
|
||
|
|
args = self.cfg.sde
|
||
|
|
device = torch.device(self.device_str)
|
||
|
|
arch_instance_dae = utils.get_arch_cells_denoising(
|
||
|
|
'res_ho_attn', True, False)
|
||
|
|
num_input_channels = self.cfg.shapelatent.latent_dim
|
||
|
|
|
||
|
|
DAE = nn.ModuleList(
|
||
|
|
[
|
||
|
|
import_model(self.cfg.latent_pts.style_prior)(args,
|
||
|
|
self.cfg.latent_pts.style_dim, self.cfg), # style prior
|
||
|
|
import_model(self.cfg.sde.prior_model)(args,
|
||
|
|
num_input_channels, self.cfg), # global prior, conditional model
|
||
|
|
])
|
||
|
|
|
||
|
|
self.dae = DAE.to(device)
|
||
|
|
|
||
|
|
# Bad solution! it is used in validate_inspect function
|
||
|
|
self.dae.num_points = self.dae[1].num_points
|
||
|
|
self.dae.num_classes = self.dae[1].num_classes
|
||
|
|
|
||
|
|
if len(self.cfg.sde.dae_checkpoint):
|
||
|
|
logger.info('Load dae checkpoint: {}',
|
||
|
|
self.cfg.sde.dae_checkpoint)
|
||
|
|
checkpoint = torch.load(
|
||
|
|
self.cfg.sde.dae_checkpoint, map_location='cpu')
|
||
|
|
self.dae.load_state_dict(checkpoint['dae_state_dict'])
|
||
|
|
|
||
|
|
self.diffusion_cont = make_diffusion(args)
|
||
|
|
self.diffusion_disc = DiffusionDiscretized(
|
||
|
|
args, self.diffusion_cont.var, self.cfg)
|
||
|
|
if not quiet:
|
||
|
|
logger.info('DAE: {}', self.dae)
|
||
|
|
logger.info('DAE: param size = %fM ' %
|
||
|
|
utils.count_parameters_in_M(self.dae))
|
||
|
|
# sync all parameters between all gpus by sending param from rank 0 to all gpus.
|
||
|
|
utils.broadcast_params(self.dae.parameters(), self.args.distributed)
|