train_neuman.py

#
# Copyright (C) 2022 Apple Inc. All rights reserved.
#

import argparse
import os
import math

import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader

from data_io import neuman_helper
from utils import utils
# from datasets import background_rays, human_rays
from options.options import str2bool
from options import options
# from models import vanilla, human_nerf
# from trainers import vanilla_nerf_trainer, human_nerf_trainer


def train_background(opt):
    assert opt.bkg_rays_ratio == 1
    coarse_net, fine_net = vanilla.build_nerf(opt)
    coarse_net = nn.DataParallel(coarse_net)
    if fine_net is not None:
        fine_net = nn.DataParallel(fine_net)
    train_split, val_split, _ = neuman_helper.create_split_files(opt.scene_dir)
    train_scene = neuman_helper.NeuManReader.read_scene(
        opt.scene_dir,
        tgt_size=opt.tgt_size,
        normalize=opt.normalize,
        bkg_range_scale=opt.bkg_range_scale,
        human_range_scale=opt.human_range_scale
    )
    train_scene.read_data_to_ram(data_list=['image', 'depth'])
    utils.add_border_mask(train_scene, iterations=opt.dilation)
    train_dset = background_rays.BackgroundRayDataset(opt, train_scene, 'train', train_split)
    val_dset = background_rays.BackgroundRayDataset(opt, train_scene, 'val', val_split)

    train_loader = DataLoader(
        train_dset,
        batch_size=1,
        shuffle=True,
        num_workers=5,
        worker_init_fn=utils.worker_init_fn,
    )
    val_loader = DataLoader(
        val_dset,
        batch_size=1,
        shuffle=True,
        num_workers=0,
        worker_init_fn=utils.worker_init_fn
    )

    optim_list = [
        {"params": coarse_net.parameters(), "lr": opt.learning_rate},
        {"params": fine_net.parameters(), "lr": opt.learning_rate},
    ]
    optim = torch.optim.Adam(optim_list, betas=(0.9, 0.999))

    trainer = vanilla_nerf_trainer.NeRFTrainer(
        opt,
        coarse_net,
        optim,
        None,
        train_loader,
        val_loader,
        train_dset,
        val_dset,
        fine_net=fine_net,
        penalize_empty_space=opt.penalize_empty_space
    )

    trainer.train()


def train_human(opt):
    train_split, val_split, _ = neuman_helper.create_split_files(opt.scene_dir)
    # import pdb; pdb.set_trace()
    
    train_scene = neuman_helper.NeuManReader.read_scene(
        opt.scene_dir,
        tgt_size=opt.tgt_size,
        normalize=opt.normalize,
        bkg_range_scale=opt.bkg_range_scale,
        human_range_scale=opt.human_range_scale,
        mask_dir=opt.mask_dir,
        smpl_type=opt.smpl_type
    )
    import pdb; pdb.set_trace()
    exit(0)
    if opt.geo_threshold < 0:
        can_bones = []
        bones = []
        for i in range(len(train_scene.captures)):
            bones.append(np.linalg.norm(train_scene.smpls[i]['joints_3d'][3] - train_scene.smpls[i]['joints_3d'][0]))
            can_bones.append(np.linalg.norm(train_scene.smpls[i]['static_joints_3d'][3] - train_scene.smpls[i]['static_joints_3d'][0]))
        opt.geo_threshold = np.mean(bones)
    poses = np.stack([item['pose'] for item in train_scene.smpls])
    betas = np.stack([item['betas'] for item in train_scene.smpls])
    raw_alignments = np.load(os.path.join(opt.scene_dir, 'alignments.npy'), allow_pickle=True).item()
    alignments = np.stack([raw_alignments[os.path.basename(cap.image_path)] for cap in train_scene.captures])
    alignments2 = np.stack([np.eye(4)] * alignments.shape[0])
    alignments2[..., :3] = alignments
    net = human_nerf.HumanNeRF(opt, poses.copy(), betas.copy(), alignments2.copy(), scale=train_scene.scale)
    device = next(net.parameters()).device
    train_scene.read_data_to_ram(data_list=['image', 'depth'])
    utils.add_border_mask(train_scene, iterations=opt.dilation)
    utils.add_pytorch3d_cache(train_scene, device)
    utils.move_smpls_to_torch(train_scene, device)

    train_dset = human_rays.HumanRayDataset(opt, train_scene, 'train', train_split)
    val_dset = human_rays.HumanRayDataset(opt, train_scene, 'val', val_split, near_far_cache=train_dset.near_far_cache)

    train_loader = DataLoader(
        train_dset,
        batch_size=1,
        shuffle=True,
        num_workers=4,
        worker_init_fn=utils.worker_init_fn,
    )
    val_loader = DataLoader(
        val_dset,
        batch_size=1,
        shuffle=True,
        num_workers=0,
        worker_init_fn=utils.worker_init_fn
    )

    assert opt.bkg_lr == 0
    if opt.train_mode == 'smpl_only':
        assert opt.offset_scale == 0
        optim_list = [
            {"params": net.poses, "lr": opt.learning_rate},
            {"params": net.coarse_human_net.parameters(), "lr": opt.learning_rate},
        ]
    elif opt.train_mode == 'smpl_and_offset':
        optim_list = [
            {"params": net.poses, "lr": opt.smpl_lr},
            {"params": net.coarse_human_net.parameters(), "lr": opt.learning_rate},
            {"params": net.offset_nets.parameters(), "lr": opt.learning_rate},
        ]
    optim = torch.optim.Adam(optim_list)

    trainer = human_nerf_trainer.HumanNeRFTrainer(
        opt,
        net,
        optim,
        train_loader,
        val_loader,
        train_dset,
        val_dset,
        interval_comp=opt.geo_threshold / np.mean(can_bones)
    )

    trainer.train()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    options.set_general_option(parser)
    options.set_nerf_option(parser)
    options.set_pe_option(parser)
    parser.add_argument('--train_mode', type=str, default='bkg', choices=['bkg', 'smpl_only', 'smpl_and_offset'])
    opt, _ = parser.parse_known_args()
    if opt.train_mode == 'bkg':
        # common args with diferent defaults
        parser.add_argument('--rays_per_batch', default=4096, type=int, help='how many samples per ray')
        parser.add_argument('--valid_iter', type=int, default=5000, help='interval of validation')
        parser.add_argument('--max_iter', type=int, default=500000, help='total training iterations')
        parser.add_argument('--body_rays_ratio', default=0.0, type=float, help='the percentage of rays on body')
        parser.add_argument('--border_rays_ratio', default=0.0, type=float, help='the percentage of rays on human border')
        parser.add_argument('--bkg_rays_ratio', default=1.0, type=float, help='the percentage of rays on background')
        parser.add_argument('--perturb', default=0, type=float, help='perturbation on samples location')
        # specific args for background
        parser.add_argument('--empty_space_loss_fn', default='mse', type=str, help='loss type')
        parser.add_argument('--use_fused_depth', default=True, type=str2bool, help='use fused depth map')
        parser.add_argument('--penalize_empty_space', default=0.1, type=float, help='penalize with depth')
        parser.add_argument('--margin', default=0.8, type=float, help='leave a margin for depth penalty')
        parser.add_argument('--ablate_nerft', type=str2bool, default=False, help='vanilla nerf with time')
    else:
        # common args with diferent defaults
        parser.add_argument('--rays_per_batch', default=1536, type=int, help='how many samples per ray')
        parser.add_argument('--valid_iter', type=int, default=1000, help='interval of validation')
        parser.add_argument('--max_iter', type=int, default=300000, help='total training iterations')
        parser.add_argument('--body_rays_ratio', default=0.95, type=float, help='the percentage of rays on body')
        parser.add_argument('--border_rays_ratio', default=0.05, type=float, help='the percentage of rays on human border')
        parser.add_argument('--bkg_rays_ratio', default=0.0, type=float, help='the percentage of rays on background')
        parser.add_argument('--perturb', default=1, type=float, help='perturbation on samples location')
        # specific args for background
        parser.add_argument('--bkg_lr', default=0, type=float, help='background model learning rate')
        parser.add_argument('--smpl_lr', default=3e-4, type=float, help='SMPL parameters learning rate')
        parser.add_argument('--geo_threshold', default=-1, type=float, help='geometry threshold for pruning far away volumes from SMPL mesh')
        parser.add_argument('--penalize_smpl_alpha', default=1, type=float, help='penalize samples inside SMPL to be opaque otherwise transparent in canonical space')
        parser.add_argument('--penalize_outside', default=True, type=str2bool, help='only penalize points inside SMPL')
        parser.add_argument('--penalize_outside_factor', default=2.0, type=float, help='penalty factor')
        parser.add_argument('--penalize_outside_loss', default='l1', type=str, help='l1 or mse')
        parser.add_argument('--dist_exponent', default=1.0, type=float, help='distance exponent for weighing the loss')
        parser.add_argument('--penalize_symmetric_alpha', default=0.1, type=float, help='enforcing the canonical space to be symmetric')
        parser.add_argument('--penalize_hard_surface', default=0.1, type=float, help='penalize with hard surface loss')
        parser.add_argument('--penalize_dummy', default=1.0, type=float, help='generate random dummy points in canonical space(see penalize_smpl_alpha)')
        parser.add_argument('--penalize_color_range', default=0.1, type=float, help='generate random dummy points in canonical space(see penalize_smpl_alpha)')
        parser.add_argument('--penalize_mask', default=0.01, type=float, help='human mask')
        parser.add_argument('--penalize_sharp_edge', default=0.1, type=float, help='canonical mask')
        parser.add_argument('--penalize_lpips', default=0.01, type=float, help='train with patch')
        parser.add_argument('--chunk', default=10000, type=int, help='chunk size per caching iteration')
        parser.add_argument('--load_background', type=str, default=None, help='load a pretrained background, for joint training')
        parser.add_argument('--load_can', type=str, default=None, help='load a pretrained canonical volume')
        parser.add_argument('--num_offset_nets', default=1, type=int, help='how many offset networks')
        parser.add_argument('--offset_scale', default=0, type=float, help='scale the predicted offset')
        parser.add_argument('--offset_scale_type', default='linear', type=str, help='no/linear/tanh')
        parser.add_argument('--offset_lim', default=1.0, type=float, help='cap the scale of predicted offset')
        parser.add_argument('--offset_delay', default=20000, type=int, help='delay the offset net training')
        parser.add_argument('--prior_knowledge_decay', type=str2bool, default=False, help='reduce prior knowledge based loss')
        parser.add_argument('--block_grad', type=str2bool, default=True, help='block gradients w.r.t occluded joints')
        parser.add_argument('--random_view', type=str2bool, default=False, help='random view point for visualization')

    parser.add_argument('--scene_dir', type=str, default=None, required=True)
    parser.add_argument('--normalize', type=str2bool, default=True, required=False, help='normalize the scene based on scene bounds')
    parser.add_argument('--bkg_range_scale', default=3, type=float, help='extend near/far range for background')
    parser.add_argument('--human_range_scale', default=1.5, type=float, help='extend near/far range for human')
    parser.add_argument('--image_height', type=int, default=None, required=False)
    parser.add_argument('--image_width', type=int, default=None, required=False)
    parser.add_argument('--white_bkg', type=str2bool, default=True, required=False)
    parser.add_argument('--samples_per_ray', default=128, type=int, help='how many samples per ray')
    parser.add_argument('--importance_samples_per_ray', default=128, type=int, help='how many importance samples per ray')
    parser.add_argument('--delay_iters', default=0, type=int, help='delay RGB loss, train with alpha/depth first')
    parser.add_argument('--learning_rate', default=5e-4, type=float, help='NeRF learning rate')
    parser.add_argument('--lrate_decay', default=250, type=int, help='NeRF learning rate decay')
    parser.add_argument('--raw_noise_std', default=0, type=float, help='add noise while rendering')
    parser.add_argument('--out_dir', default='./out', type=str, help='output dir')
    parser.add_argument('--name', default='dummy', type=str, help='name of run')
    parser.add_argument('--resume', type=str2bool, default=False, help='resume training with same model name')
    parser.add_argument('--load_weights', type=str, default=None, help='load a pretrained set of weights, you need to provide the model id')
    parser.add_argument('--mask_dir', default='segmentations', type=str, help='mask folder')
    parser.add_argument('--smpl_type', default='optimized', type=str, choices=['romp', 'optimized'], help='smpl source')
    parser.add_argument('--dilation', type=int, default=30, help='mask dilation')

    opt = parser.parse_args()
    if opt.image_height is not None or opt.image_width is not None:
        assert opt.image_height is not None
        assert opt.image_width is not None
        opt.tgt_size = (opt.image_height, opt.image_width)
    else:
        opt.tgt_size = None
    opt.out = os.path.join(opt.out_dir, opt.name)
    opt.tb_dir = os.path.join(opt.out_dir, 'tensorboard_out', opt.name)
    if opt.load_weights is not None:
        opt.load_weights_path = os.path.join(opt.out_dir, opt.load_weights, 'checkpoint.pth.tar')
        assert os.path.isfile(opt.load_weights_path), opt.load_weights_path
    elif opt.resume:
        opt.load_weights_path = os.path.join(opt.out_dir, f'{opt.name}', 'checkpoint.pth.tar')
        assert os.path.isfile(opt.load_weights_path), opt.load_weights_path
    assert math.isclose(opt.body_rays_ratio + opt.border_rays_ratio + opt.bkg_rays_ratio, 1.0), f'{opt.body_rays_ratio + opt.border_rays_ratio + opt.bkg_rays_ratio}'

    if hasattr(opt, 'ablate_nerft') and opt.ablate_nerft:
        assert opt.raw_pos_dim == 4
        assert opt.train_mode == 'bkg'
    assert opt.normalize == True

    options.print_opt(opt)
    options.save_opt(opt)
    if opt.train_mode == 'bkg':
        train_background(opt)
    elif opt.train_mode in ['smpl_only', 'smpl_and_offset']:
        # print(opt)
        # import pdb; pdb.set_trace();
        train_human(opt)
    else:
        raise ValueError