train.py

#-*-coding:utf-8-*-
"""
    NeRF with mip ideas baseline model
"""
import os
import torch
from torch import optim
from torchvision import transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F

from py.timer import Timer
from py.nerf_base import DecayLrScheduler, NeRF
from py.dataset import CustomDataSet, AdaptiveResize
from torchvision.utils import save_image
from py.nerf_helper import nan_hook, saveModel
from py.mip_methods import maxBlurFilter
from py.procedures import render_image, get_parser, render_only
from py.utils import fov2Focal, getSummaryWriter, validSampler, randomFromOneImage, inverseSample
from py.addtional import getBounds, ProposalLoss, ProposalNetwork, SoftL1Loss, LossPSNR
from torch.cuda.amp.grad_scaler import GradScaler
from torch.cuda.amp import autocast as autocast

default_chkpt_path = "./check_points/"
default_model_path = "./model/"

def main(args):
    epochs              = args.epochs
    sample_ray_num      = args.sample_ray_num
    coarse_sample_pnum  = args.coarse_sample_pnum
    fine_sample_pnum    = args.fine_sample_pnum
    near_t              = args.near
    far_t               = args.far
    center_crop_iter    = args.center_crop_iter
    center_crop         = (args.center_crop_x, args.center_crop_y)

    eval_time           = args.eval_time
    dataset_name        = args.dataset_name
    load_path_mip       = default_chkpt_path + args.name + "_mip.pt"
    load_path_prop      = default_chkpt_path + args.name + "_prop.pt"
    # Bool options
    del_dir             = args.del_dir
    use_load            = args.load
    debugging           = args.debug
    output_time         = args.output_time
    use_amp             = (args.use_scaler and (not debugging))
    img_scale           = args.img_scale
    scene_scale         = args.scene_scale
    use_white_bkg       = args.white_bkg
    opt_mode            = args.opt_mode        
    use_ref_nerf        = args.ref_nerf
    grad_clip_val       = args.grad_clip
    render_depth        = args.render_depth
    render_normal       = args.render_normal
    prop_normal         = args.prop_normal
    actual_lr           = args.lr * sample_ray_num / 512        # bigger batch -> higher lr (linearity)
    train_cnt, ep_start = None, None

    if use_amp:
        from apex import amp

    if not torch.cuda.is_available():
        print("CUDA not available.")
        exit(-1)
    
    # ======= instantiate model =====
    # NOTE: model is recommended to have loadFromFile method
    if use_ref_nerf:
        from py.ref_model import RefNeRF, WeightedNormalLoss, BackFaceLoss
        normal_loss_func = WeightedNormalLoss(True)
        bf_loss_func = BackFaceLoss() 
        mip_net = RefNeRF(10, args.ide_level, hidden_unit = 256, perturb_bottle_neck_w = args.bottle_neck_noise, use_srgb = args.use_srgb).cuda()
    else:
        from py.mip_model import MipNeRF
        mip_net = MipNeRF(10, 4, hidden_unit = 256).cuda()
    prop_net = ProposalNetwork(10, hidden_unit = 256).cuda()

    if debugging:
        for submodule in mip_net.modules():
            submodule.register_forward_hook(nan_hook)
        torch.autograd.set_detect_anomaly(True)

    # ======= Loss function ==========
    loss_func = SoftL1Loss()
    prop_loss_func = ProposalLoss().cuda()
    mse2psnr = LossPSNR()
    # ======= Optimizer and scheduler ========

    transform_funcs = transforms.Compose([
        AdaptiveResize(img_scale),
        transforms.ToTensor(),
    ])

    # 数据集加载
    trainset = CustomDataSet("../dataset/refnerf/%s/"%(dataset_name), transform_funcs, scene_scale, True, use_alpha = False, white_bkg = use_white_bkg)
    testset = CustomDataSet("../dataset/refnerf/%s/"%(dataset_name), transform_funcs, scene_scale, False, use_alpha = False, white_bkg = use_white_bkg)
    cam_fov_train, train_cam_tf = trainset.getCameraParam()
    r_c = trainset.r_c()
    train_cam_tf = train_cam_tf.cuda()
    del train_cam_tf
    train_loader = DataLoader(trainset, 1, shuffle = True, num_workers = 4)
    cam_fov_test, _ = testset.getCameraParam()
    
    train_focal = fov2Focal(cam_fov_train, r_c)
    test_focal = fov2Focal(cam_fov_test, r_c)
    print("Training focal: (%f, %f), image size: (w: %d, h: %d)"%(train_focal[0], train_focal[1], r_c[1], r_c[0]))

    grad_vars = list(mip_net.parameters()) + list(prop_net.parameters())
    opt = optim.Adam(params = grad_vars, lr = actual_lr, betas=(0.9, 0.999))
    def grad_clip_func(parameters, grad_clip):
        if grad_clip > 0.:
            torch.nn.utils.clip_grad_norm_(parameters, grad_clip)

    if use_amp:
        if opt_mode.lower() != "native":
            [mip_net, prop_net], opt = amp.initialize([mip_net, prop_net], opt, opt_level=opt_mode)
        else:
            scaler = GradScaler()
    if use_load == True and os.path.exists(load_path_mip) and os.path.exists(load_path_prop):
        train_cnt, ep_start = mip_net.loadFromFile(load_path_mip, use_amp and opt_mode != "native", opt, ["train_cnt", "epoch"])
        prop_net.loadFromFile(load_path_prop, use_amp and opt_mode != "native")
    else:
        print("Not loading or load path '%s' / '%s' does not exist."%(load_path_mip, load_path_prop))
    lr_sch = DecayLrScheduler(args.min_ratio, args.decay_rate, args.decay_step, actual_lr, args.warmup_step)

    test_views = []
    for i in (1, 4):
        test_views.append(testset[i])
    torch.cuda.empty_cache()

    # ====== tensorboard summary writer ======
    writer = getSummaryWriter(epochs, del_dir)
    train_set_len = len(trainset)

    if ep_start is None:
        ep_start = 0
        train_cnt = ep_start * train_set_len
    test_cnt = ep_start // 20
    train_timer, eval_timer, epoch_timer = Timer(5), Timer(5), Timer(3)
    for ep in range(ep_start, epochs):
        epoch_timer.tic()
        for i, (train_img, train_tf) in enumerate(train_loader):
            train_timer.tic()
            train_img = train_img.cuda().squeeze(0)
            train_tf = train_tf.cuda().squeeze(0)
            now_crop = (center_crop if train_cnt < center_crop_iter else (1., 1.))
            valid_pixels, valid_coords = randomFromOneImage(train_img, now_crop)

            # sample one more t to form (coarse_sample_pnum) proposal interval
            coarse_samples, coarse_lengths, rgb_targets, coarse_cam_rays = validSampler(
                valid_pixels, valid_coords, train_tf, sample_ray_num, coarse_sample_pnum, train_focal, near_t, far_t, True
            )
            # output 
            def run(is_ref_model = False):
                coarse_samples.requires_grad = prop_normal                  
                density = prop_net.forward(coarse_samples)
                prop_weights_raw = ProposalNetwork.get_weights(density, coarse_lengths, coarse_cam_rays[:, 3:])      # (ray_num, num of proposal interval)
                prop_weights = maxBlurFilter(prop_weights_raw, 0.03)

                coarse_normal_loss = normal_loss = bf_loss = 0.
                fine_lengths, below_idxs = inverseSample(prop_weights, coarse_lengths, fine_sample_pnum + 1, sort = True)
                if is_ref_model == True:
                    fine_samples, fine_lengths, below_idxs, sort_ids = NeRF.coarseFineMerge(coarse_cam_rays, coarse_lengths, fine_lengths, below_idxs)
                    fine_pos, fine_dir = fine_samples.split((3, 3), dim = -1)
                    fine_pos.requires_grad = True
                    fine_rgbo, pred_normal = mip_net.forward(fine_pos, fine_dir)
                    density_grad = -RefNeRF.get_grad(fine_rgbo[..., -1], fine_pos)
                    if prop_normal == True:
                        coarse_grad = -RefNeRF.get_grad(density, coarse_samples)
                        coarse_pt_fine_grad = RefNeRF.coarse_grad_select(density_grad, sort_ids, coarse_sample_pnum)
                        coarse_normal_loss = normal_loss_func(weights, coarse_pt_fine_grad.detach(), coarse_grad)
                    fine_rgbo[..., -1] = F.softplus(fine_rgbo[..., -1] + 0.5)
                    fine_rendered, weights, _ = NeRF.render(fine_rgbo, fine_lengths, coarse_cam_rays[:, 3:], mip_net.density_act)
                    normal_loss = normal_loss_func(weights, density_grad, pred_normal)
                    bf_loss = bf_loss_func(weights, pred_normal, fine_dir)
                else:
                    fine_lengths = fine_lengths[..., :-1]
                    fine_samples = NeRF.length2pts(coarse_cam_rays, fine_lengths)
                    fine_rgbo = mip_net.forward(fine_samples)
                    fine_rendered, weights, _ = NeRF.render(fine_rgbo, fine_lengths, coarse_cam_rays[:, 3:])
                weight_bounds:torch.Tensor = getBounds(prop_weights, below_idxs)             # output shape: (ray_num, num of conical frustum)
                opt.zero_grad()
                img_loss:torch.Tensor = loss_func(fine_rendered, rgb_targets)                           # stop the gradient of NeRF MLP 

                prop_loss:torch.Tensor = prop_loss_func(weight_bounds, weights.detach())                # stop the gradient of NeRF MLP 
                loss:torch.Tensor = prop_loss + img_loss + 5e-4 * (normal_loss + 0.1 * coarse_normal_loss)  + 0.2 * bf_loss
                return loss, img_loss
            if use_amp:
                if opt_mode == "native":
                    with autocast():
                        loss, img_loss = run(use_ref_nerf)
                        scaler.scale(loss).backward()
                        grad_clip_func(grad_vars, grad_clip_val)
                        scaler.step(opt)
                        scaler.update()
                else:
                    loss, img_loss = run(use_ref_nerf)
                    with amp.scale_loss(loss, opt) as scaled_loss:
                        scaled_loss.backward()
                    grad_clip_func(grad_vars, grad_clip_val)
                    opt.step()
            else:
                loss, img_loss = run(use_ref_nerf)
                loss.backward()
                grad_clip_func(grad_vars, grad_clip_val)
                opt.step()

            train_timer.toc()

            opt, new_lr = lr_sch.update_opt_lr(train_cnt, opt)
            if train_cnt % eval_time == 1:
                # ========= Evaluation output ========
                remaining_cnt = (epochs - ep - 1) * train_set_len + train_set_len - i
                psnr = mse2psnr(img_loss)
                print("Traning Epoch: %4d / %4d\t Iter %4d / %4d\ttrain loss: %.4f\tPSNR: %.3lf\tlr:%.7lf\tcenter crop:%.1lf, %.1lf\tremaining train time:%s"%(
                        ep, epochs, i, train_set_len, loss.item(), psnr, new_lr, now_crop[0], now_crop[1], train_timer.remaining_time(remaining_cnt)
                ))
                writer.add_scalar('Train Loss', loss, train_cnt)
                writer.add_scalar('Learning Rate', new_lr, train_cnt)
                writer.add_scalar('PSNR', psnr, train_cnt)
            train_cnt += 1

        if ((ep % output_time == 0) or ep == epochs - 1) and ep > ep_start:
            mip_net.eval()
            prop_net.eval()
            with torch.no_grad():
                eval_timer.tic()
                test_results = []
                test_loss = torch.zeros(1).cuda()
                for test_img, test_tf in test_views:
                    test_result = render_image(
                        mip_net, prop_net, test_tf.cuda(), r_c, test_focal, near_t, far_t, fine_sample_pnum, 
                        white_bkg = use_white_bkg, render_depth = render_depth, render_normal = render_normal
                    )
                    for value in test_result.values():
                        test_results.append(value)
                    test_loss += loss_func(test_result["rgb"], test_img.cuda())
                eval_timer.toc()
                writer.add_scalar('Test Loss', loss, test_cnt)
                print("Evaluation in epoch: %4d / %4d\t, test counter: %d test loss: %.4f\taverage time: %.4lf\tremaining eval time:%s"%(
                        ep, epochs, test_cnt, test_loss.item() / 2, eval_timer.get_mean_time(), eval_timer.remaining_time(epochs - ep - 1)
                ))
                images_to_save = []
                images_to_save.extend(test_results)
                save_image(images_to_save, "./output/result_%03d.png"%(test_cnt), nrow = 1 + render_normal + render_depth)
                # ======== Saving checkpoints ========
                saveModel(mip_net,  "%schkpt_%d_mip.pt"%(default_chkpt_path, train_cnt), {"train_cnt": train_cnt, "epoch": ep}, opt = opt, amp = (amp) if use_amp and opt_mode != "native" else None)
                saveModel(prop_net,  "%schkpt_%d_prop.pt"%(default_chkpt_path, train_cnt), opt = None, amp = (amp) if use_amp and opt_mode != "native" else None)
                test_cnt += 1
            mip_net.train()
            prop_net.train()
        epoch_timer.toc()
        print("Epoch %4d / %4d completed\trunning time for this epoch: %.5lf\testimated remaining time: %s"
                %(ep, epochs, epoch_timer.get_mean_time(), epoch_timer.remaining_time(epochs - ep - 1))
        )
    # ======== Saving the model ========
    saveModel(mip_net, "%smodel_%d_mip.pth"%(default_model_path, 2), opt = opt, amp = (amp) if use_amp and opt_mode != "native" else None)
    saveModel(prop_net, "%smodel_%d_prop.pth"%(default_model_path, 2), opt = None, amp = (amp) if use_amp and opt_mode != "native" else None)
    writer.close()
    print("Output completed.")

if __name__ == "__main__":
    parser = get_parser()
    args = parser.parse_args()      # spherical rendering is disabled (for now)
    do_render = args.do_render
    opt_mode = args.opt_mode
    if do_render:
        render_only(args, default_model_path, opt_mode)
    else:
        main(args)