train.py

# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. 
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction, 
# disclosure or distribution of this material and related documentation 
# without an express license agreement from NVIDIA CORPORATION or 
# its affiliates is strictly prohibited.

import os
import time
import argparse
import json

import numpy as np
import torch
import nvdiffrast.torch as dr
import xatlas

# Import data readers / generators
from dataset.dataset_mesh import DatasetMesh
from dataset.dataset_nerf import DatasetNERF
from dataset.dataset_llff import DatasetLLFF

# Import topology / geometry trainers
from geometry.dmtet import DMTetGeometry
from geometry.dlmesh import DLMesh

import render.renderutils as ru
from render import obj
from render import material
from render import util
from render import mesh
from render import texture
from render import mlptexture
from render import light
from render import render

RADIUS = 3.0

# Enable to debug back-prop anomalies
# torch.autograd.set_detect_anomaly(True)

###############################################################################
# Loss setup
###############################################################################

@torch.no_grad()
def createLoss(FLAGS):
    if FLAGS.loss == "smape":
        return lambda img, ref: ru.image_loss(img, ref, loss='smape', tonemapper='none')
    elif FLAGS.loss == "mse":
        return lambda img, ref: ru.image_loss(img, ref, loss='mse', tonemapper='none')
    elif FLAGS.loss == "logl1":
        return lambda img, ref: ru.image_loss(img, ref, loss='l1', tonemapper='log_srgb')
    elif FLAGS.loss == "logl2":
        return lambda img, ref: ru.image_loss(img, ref, loss='mse', tonemapper='log_srgb')
    elif FLAGS.loss == "relmse":
        return lambda img, ref: ru.image_loss(img, ref, loss='relmse', tonemapper='none')
    else:
        assert False

###############################################################################
# Mix background into a dataset image
###############################################################################

@torch.no_grad()
def prepare_batch(target, bg_type='black'):
    assert len(target['img'].shape) == 4, "Image shape should be [n, h, w, c]"
    if bg_type == 'checker':
        background = torch.tensor(util.checkerboard(target['img'].shape[1:3], 8), dtype=torch.float32, device='cuda')[None, ...]
    elif bg_type == 'black':
        background = torch.zeros(target['img'].shape[0:3] + (3,), dtype=torch.float32, device='cuda')
    elif bg_type == 'white':
        background = torch.ones(target['img'].shape[0:3] + (3,), dtype=torch.float32, device='cuda')
    elif bg_type == 'reference':
        background = target['img'][..., 0:3]
    elif bg_type == 'random':
        background = torch.rand(target['img'].shape[0:3] + (3,), dtype=torch.float32, device='cuda')
    else:
        assert False, "Unknown background type %s" % bg_type

    target['mv'] = target['mv'].cuda()
    target['mvp'] = target['mvp'].cuda()
    target['campos'] = target['campos'].cuda()
    target['img'] = target['img'].cuda()
    target['background'] = background

    target['img'] = torch.cat((torch.lerp(background, target['img'][..., 0:3], target['img'][..., 3:4]), target['img'][..., 3:4]), dim=-1)

    return target

###############################################################################
# UV - map geometry & convert to a mesh
###############################################################################

@torch.no_grad()
def xatlas_uvmap(glctx, geometry, mat, FLAGS):
    eval_mesh = geometry.getMesh(mat)
    
    # Create uvs with xatlas
    v_pos = eval_mesh.v_pos.detach().cpu().numpy()
    t_pos_idx = eval_mesh.t_pos_idx.detach().cpu().numpy()
    vmapping, indices, uvs = xatlas.parametrize(v_pos, t_pos_idx)

    # Convert to tensors
    indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
    
    uvs = torch.tensor(uvs, dtype=torch.float32, device='cuda')
    faces = torch.tensor(indices_int64, dtype=torch.int64, device='cuda')

    new_mesh = mesh.Mesh(v_tex=uvs, t_tex_idx=faces, base=eval_mesh)

    mask, kd, ks, normal = render.render_uv(glctx, new_mesh, FLAGS.texture_res, eval_mesh.material['kd_ks_normal'])
    
    if FLAGS.layers > 1:
        kd = torch.cat((kd, torch.rand_like(kd[...,0:1])), dim=-1)

    kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
    ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
    nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')

    new_mesh.material = material.Material({
        'bsdf'   : mat['bsdf'],
        'kd'     : texture.Texture2D(kd, min_max=[kd_min, kd_max]),
        'ks'     : texture.Texture2D(ks, min_max=[ks_min, ks_max]),
        'normal' : texture.Texture2D(normal, min_max=[nrm_min, nrm_max])
    })

    return new_mesh

###############################################################################
# Utility functions for material
###############################################################################

def initial_guess_material(geometry, mlp, FLAGS, init_mat=None):
    kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
    ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
    nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')
    if mlp:
        mlp_min = torch.cat((kd_min[0:3], ks_min, nrm_min), dim=0)
        mlp_max = torch.cat((kd_max[0:3], ks_max, nrm_max), dim=0)
        mlp_map_opt = mlptexture.MLPTexture3D(geometry.getAABB(), channels=9, min_max=[mlp_min, mlp_max])
        mat =  material.Material({'kd_ks_normal' : mlp_map_opt})
    else:
        # Setup Kd (albedo) and Ks (x, roughness, metalness) textures
        if FLAGS.random_textures or init_mat is None:
            num_channels = 4 if FLAGS.layers > 1 else 3
            kd_init = torch.rand(size=FLAGS.texture_res + [num_channels], device='cuda') * (kd_max - kd_min)[None, None, 0:num_channels] + kd_min[None, None, 0:num_channels]
            kd_map_opt = texture.create_trainable(kd_init , FLAGS.texture_res, not FLAGS.custom_mip, [kd_min, kd_max])

            ksR = np.random.uniform(size=FLAGS.texture_res + [1], low=0.0, high=0.01)
            ksG = np.random.uniform(size=FLAGS.texture_res + [1], low=ks_min[1].cpu(), high=ks_max[1].cpu())
            ksB = np.random.uniform(size=FLAGS.texture_res + [1], low=ks_min[2].cpu(), high=ks_max[2].cpu())

            ks_map_opt = texture.create_trainable(np.concatenate((ksR, ksG, ksB), axis=2), FLAGS.texture_res, not FLAGS.custom_mip, [ks_min, ks_max])
        else:
            kd_map_opt = texture.create_trainable(init_mat['kd'], FLAGS.texture_res, not FLAGS.custom_mip, [kd_min, kd_max])
            ks_map_opt = texture.create_trainable(init_mat['ks'], FLAGS.texture_res, not FLAGS.custom_mip, [ks_min, ks_max])

        # Setup normal map
        if FLAGS.random_textures or init_mat is None or 'normal' not in init_mat:
            normal_map_opt = texture.create_trainable(np.array([0, 0, 1]), FLAGS.texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])
        else:
            normal_map_opt = texture.create_trainable(init_mat['normal'], FLAGS.texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])

        mat = material.Material({
            'kd'     : kd_map_opt,
            'ks'     : ks_map_opt,
            'normal' : normal_map_opt
        })

    if init_mat is not None:
        mat['bsdf'] = init_mat['bsdf']
    else:
        mat['bsdf'] = 'pbr'

    return mat

###############################################################################
# Validation & testing
###############################################################################

def validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS):
    result_dict = {}
    with torch.no_grad():
        lgt.build_mips()
        if FLAGS.camera_space_light:
            lgt.xfm(target['mv'])

        buffers = geometry.render(glctx, target, lgt, opt_material)

        result_dict['ref'] = util.rgb_to_srgb(target['img'][...,0:3])[0]
        result_dict['opt'] = util.rgb_to_srgb(buffers['shaded'][...,0:3])[0]
        result_image = torch.cat([result_dict['opt'], result_dict['ref']], axis=1)

        if FLAGS.display is not None:
            white_bg = torch.ones_like(target['background'])
            for layer in FLAGS.display:
                if 'latlong' in layer and layer['latlong']:
                    if isinstance(lgt, light.EnvironmentLight):
                        result_dict['light_image'] = util.cubemap_to_latlong(lgt.base, FLAGS.display_res)
                    result_image = torch.cat([result_image, result_dict['light_image']], axis=1)
                elif 'relight' in layer:
                    if not isinstance(layer['relight'], light.EnvironmentLight):
                        layer['relight'] = light.load_env(layer['relight'])
                    img = geometry.render(glctx, target, layer['relight'], opt_material)
                    result_dict['relight'] = util.rgb_to_srgb(img[..., 0:3])[0]
                    result_image = torch.cat([result_image, result_dict['relight']], axis=1)
                elif 'bsdf' in layer:
                    buffers = geometry.render(glctx, target, lgt, opt_material, bsdf=layer['bsdf'])
                    if layer['bsdf'] == 'kd':
                        result_dict[layer['bsdf']] = util.rgb_to_srgb(buffers['shaded'][0, ..., 0:3])  
                    elif layer['bsdf'] == 'normal':
                        result_dict[layer['bsdf']] = (buffers['shaded'][0, ..., 0:3] + 1) * 0.5
                    else:
                        result_dict[layer['bsdf']] = buffers['shaded'][0, ..., 0:3]
                    result_image = torch.cat([result_image, result_dict[layer['bsdf']]], axis=1)
   
        return result_image, result_dict

def validate(glctx, geometry, opt_material, lgt, dataset_validate, out_dir, FLAGS):

    # ==============================================================================================
    #  Validation loop
    # ==============================================================================================
    mse_values = []
    psnr_values = []

    dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_validate.collate)

    os.makedirs(out_dir, exist_ok=True)
    with open(os.path.join(out_dir, 'metrics.txt'), 'w') as fout:
        fout.write('ID, MSE, PSNR\n')

        print("Running validation")
        for it, target in enumerate(dataloader_validate):

            # Mix validation background
            target = prepare_batch(target, FLAGS.background)

            result_image, result_dict = validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS)
           
            # Compute metrics
            opt = torch.clamp(result_dict['opt'], 0.0, 1.0) 
            ref = torch.clamp(result_dict['ref'], 0.0, 1.0)

            mse = torch.nn.functional.mse_loss(opt, ref, size_average=None, reduce=None, reduction='mean').item()
            mse_values.append(float(mse))
            psnr = util.mse_to_psnr(mse)
            psnr_values.append(float(psnr))

            line = "%d, %1.8f, %1.8f\n" % (it, mse, psnr)
            fout.write(str(line))

            for k in result_dict.keys():
                np_img = result_dict[k].detach().cpu().numpy()
                util.save_image(out_dir + '/' + ('val_%06d_%s.png' % (it, k)), np_img)

        avg_mse = np.mean(np.array(mse_values))
        avg_psnr = np.mean(np.array(psnr_values))
        line = "AVERAGES: %1.4f, %2.3f\n" % (avg_mse, avg_psnr)
        fout.write(str(line))
        print("MSE,      PSNR")
        print("%1.8f, %2.3f" % (avg_mse, avg_psnr))
    return avg_psnr

###############################################################################
# Main shape fitter function / optimization loop
###############################################################################

class Trainer(torch.nn.Module):
    def __init__(self, glctx, geometry, lgt, mat, optimize_geometry, optimize_light, image_loss_fn, FLAGS):
        super(Trainer, self).__init__()

        self.glctx = glctx
        self.geometry = geometry
        self.light = lgt
        self.material = mat
        self.optimize_geometry = optimize_geometry
        self.optimize_light = optimize_light
        self.image_loss_fn = image_loss_fn
        self.FLAGS = FLAGS

        if not self.optimize_light:
            with torch.no_grad():
                self.light.build_mips()

        self.params = list(self.material.parameters())
        self.params += list(self.light.parameters()) if optimize_light else []
        self.geo_params = list(self.geometry.parameters()) if optimize_geometry else []

    def forward(self, target, it):
        if self.optimize_light:
            self.light.build_mips()
            if self.FLAGS.camera_space_light:
                self.light.xfm(target['mv'])

        return self.geometry.tick(glctx, target, self.light, self.material, self.image_loss_fn, it)

def optimize_mesh(
    glctx,
    geometry,
    opt_material,
    lgt,
    dataset_train,
    dataset_validate,
    FLAGS,
    warmup_iter=0,
    log_interval=10,
    pass_idx=0,
    pass_name="",
    optimize_light=True,
    optimize_geometry=True
    ):

    # ==============================================================================================
    #  Setup torch optimizer
    # ==============================================================================================

    learning_rate = FLAGS.learning_rate[pass_idx] if isinstance(FLAGS.learning_rate, list) or isinstance(FLAGS.learning_rate, tuple) else FLAGS.learning_rate
    learning_rate_pos = learning_rate[0] if isinstance(learning_rate, list) or isinstance(learning_rate, tuple) else learning_rate
    learning_rate_mat = learning_rate[1] if isinstance(learning_rate, list) or isinstance(learning_rate, tuple) else learning_rate

    def lr_schedule(iter, fraction):
        if iter < warmup_iter:
            return iter / warmup_iter 
        return max(0.0, 10**(-(iter - warmup_iter)*0.0002)) # Exponential falloff from [1.0, 0.1] over 5k epochs.    

    # ==============================================================================================
    #  Image loss
    # ==============================================================================================
    image_loss_fn = createLoss(FLAGS)

    trainer_noddp = Trainer(glctx, geometry, lgt, opt_material, optimize_geometry, optimize_light, image_loss_fn, FLAGS)

    if FLAGS.multi_gpu: 
        # Multi GPU training mode
        import apex
        from apex.parallel import DistributedDataParallel as DDP

        trainer = DDP(trainer_noddp)
        trainer.train()
        if optimize_geometry:
            optimizer_mesh = apex.optimizers.FusedAdam(trainer_noddp.geo_params, lr=learning_rate_pos)
            scheduler_mesh = torch.optim.lr_scheduler.LambdaLR(optimizer_mesh, lr_lambda=lambda x: lr_schedule(x, 0.9)) 

        optimizer = apex.optimizers.FusedAdam(trainer_noddp.params, lr=learning_rate_mat)
        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_schedule(x, 0.9)) 
    else:
        # Single GPU training mode
        trainer = trainer_noddp
        if optimize_geometry:
            optimizer_mesh = torch.optim.Adam(trainer_noddp.geo_params, lr=learning_rate_pos)
            scheduler_mesh = torch.optim.lr_scheduler.LambdaLR(optimizer_mesh, lr_lambda=lambda x: lr_schedule(x, 0.9)) 

        optimizer = torch.optim.Adam(trainer_noddp.params, lr=learning_rate_mat)
        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_schedule(x, 0.9)) 

    # ==============================================================================================
    #  Training loop
    # ==============================================================================================
    img_cnt = 0
    img_loss_vec = []
    reg_loss_vec = []
    iter_dur_vec = []

    dataloader_train    = torch.utils.data.DataLoader(dataset_train, batch_size=FLAGS.batch, collate_fn=dataset_train.collate, shuffle=True)
    dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_train.collate)

    def cycle(iterable):
        iterator = iter(iterable)
        while True:
            try:
                yield next(iterator)
            except StopIteration:
                iterator = iter(iterable)

    v_it = cycle(dataloader_validate)

    for it, target in enumerate(dataloader_train):

        # Mix randomized background into dataset image
        target = prepare_batch(target, 'random')

        # ==============================================================================================
        #  Display / save outputs. Do it before training so we get initial meshes
        # ==============================================================================================

        # Show/save image before training step (want to get correct rendering of input)
        if FLAGS.local_rank == 0:
            display_image = FLAGS.display_interval and (it % FLAGS.display_interval == 0)
            save_image = FLAGS.save_interval and (it % FLAGS.save_interval == 0)
            if display_image or save_image:
                result_image, result_dict = validate_itr(glctx, prepare_batch(next(v_it), FLAGS.background), geometry, opt_material, lgt, FLAGS)
                np_result_image = result_image.detach().cpu().numpy()
                if display_image:
                    util.display_image(np_result_image, title='%d / %d' % (it, FLAGS.iter))
                if save_image:
                    util.save_image(FLAGS.out_dir + '/' + ('img_%s_%06d.png' % (pass_name, img_cnt)), np_result_image)
                    img_cnt = img_cnt+1

        iter_start_time = time.time()

        # ==============================================================================================
        #  Zero gradients
        # ==============================================================================================
        optimizer.zero_grad()
        if optimize_geometry:
            optimizer_mesh.zero_grad()

        # ==============================================================================================
        #  Training
        # ==============================================================================================
        img_loss, reg_loss = trainer(target, it)

        # ==============================================================================================
        #  Final loss
        # ==============================================================================================
        total_loss = img_loss + reg_loss

        img_loss_vec.append(img_loss.item())
        reg_loss_vec.append(reg_loss.item())

        # ==============================================================================================
        #  Backpropagate
        # ==============================================================================================
        total_loss.backward()
        if hasattr(lgt, 'base') and lgt.base.grad is not None and optimize_light:
            lgt.base.grad *= 64
        if 'kd_ks_normal' in opt_material:
            opt_material['kd_ks_normal'].encoder.params.grad /= 8.0

        optimizer.step()
        scheduler.step()

        if optimize_geometry:
            optimizer_mesh.step()
            scheduler_mesh.step()

        # ==============================================================================================
        #  Clamp trainables to reasonable range
        # ==============================================================================================
        with torch.no_grad():
            if 'kd' in opt_material:
                opt_material['kd'].clamp_()
            if 'ks' in opt_material:
                opt_material['ks'].clamp_()
            if 'normal' in opt_material:
                opt_material['normal'].clamp_()
                opt_material['normal'].normalize_()
            if lgt is not None:
                lgt.clamp_(min=0.0)

        torch.cuda.current_stream().synchronize()
        iter_dur_vec.append(time.time() - iter_start_time)

        # ==============================================================================================
        #  Logging
        # ==============================================================================================
        if it % log_interval == 0 and FLAGS.local_rank == 0:
            img_loss_avg = np.mean(np.asarray(img_loss_vec[-log_interval:]))
            reg_loss_avg = np.mean(np.asarray(reg_loss_vec[-log_interval:]))
            iter_dur_avg = np.mean(np.asarray(iter_dur_vec[-log_interval:]))
            
            remaining_time = (FLAGS.iter-it)*iter_dur_avg
            print("iter=%5d, img_loss=%.6f, reg_loss=%.6f, lr=%.5f, time=%.1f ms, rem=%s" % 
                (it, img_loss_avg, reg_loss_avg, optimizer.param_groups[0]['lr'], iter_dur_avg*1000, util.time_to_text(remaining_time)))

    return geometry, opt_material

#----------------------------------------------------------------------------
# Main function.
#----------------------------------------------------------------------------

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='nvdiffrec')
    parser.add_argument('--config', type=str, default=None, help='Config file')
    parser.add_argument('-i', '--iter', type=int, default=5000)
    parser.add_argument('-b', '--batch', type=int, default=1)
    parser.add_argument('-s', '--spp', type=int, default=1)
    parser.add_argument('-l', '--layers', type=int, default=1)
    parser.add_argument('-r', '--train-res', nargs=2, type=int, default=[512, 512])
    parser.add_argument('-dr', '--display-res', type=int, default=None)
    parser.add_argument('-tr', '--texture-res', nargs=2, type=int, default=[1024, 1024])
    parser.add_argument('-di', '--display-interval', type=int, default=0)
    parser.add_argument('-si', '--save-interval', type=int, default=1000)
    parser.add_argument('-lr', '--learning-rate', type=float, default=0.01)
    parser.add_argument('-mr', '--min-roughness', type=float, default=0.08)
    parser.add_argument('-mip', '--custom-mip', action='store_true', default=False)
    parser.add_argument('-rt', '--random-textures', action='store_true', default=False)
    parser.add_argument('-bg', '--background', default='checker', choices=['black', 'white', 'checker', 'reference'])
    parser.add_argument('--loss', default='logl1', choices=['logl1', 'logl2', 'mse', 'smape', 'relmse'])
    parser.add_argument('-o', '--out-dir', type=str, default=None)
    parser.add_argument('-rm', '--ref_mesh', type=str)
    parser.add_argument('-bm', '--base-mesh', type=str, default=None)
    parser.add_argument('--validate', type=bool, default=True)
    
    FLAGS = parser.parse_args()

    FLAGS.mtl_override        = None                     # Override material of model
    FLAGS.dmtet_grid          = 64                       # Resolution of initial tet grid. We provide 64 and 128 resolution grids. Other resolutions can be generated with https://github.com/crawforddoran/quartet
    FLAGS.mesh_scale          = 2.1                      # Scale of tet grid box. Adjust to cover the model
    FLAGS.env_scale           = 1.0                      # Env map intensity multiplier
    FLAGS.envmap              = None                     # HDR environment probe
    FLAGS.display             = None                     # Conf validation window/display. E.g. [{"relight" : <path to envlight>}]
    FLAGS.camera_space_light  = False                    # Fixed light in camera space. This is needed for setups like ethiopian head where the scanned object rotates on a stand.
    FLAGS.lock_light          = False                    # Disable light optimization in the second pass
    FLAGS.lock_pos            = False                    # Disable vertex position optimization in the second pass
    FLAGS.sdf_regularizer     = 0.2                      # Weight for sdf regularizer (see paper for details)
    FLAGS.laplace             = "relative"               # Mesh Laplacian ["absolute", "relative"]
    FLAGS.laplace_scale       = 10000.0                  # Weight for sdf regularizer. Default is relative with large weight
    FLAGS.pre_load            = True                     # Pre-load entire dataset into memory for faster training
    FLAGS.kd_min              = [ 0.0,  0.0,  0.0,  0.0] # Limits for kd
    FLAGS.kd_max              = [ 1.0,  1.0,  1.0,  1.0]
    FLAGS.ks_min              = [ 0.0, 0.08,  0.0]       # Limits for ks
    FLAGS.ks_max              = [ 1.0,  1.0,  1.0]
    FLAGS.nrm_min             = [-1.0, -1.0,  0.0]       # Limits for normal map
    FLAGS.nrm_max             = [ 1.0,  1.0,  1.0]
    FLAGS.cam_near_far        = [0.1, 1000.0]
    FLAGS.learn_light         = True

    FLAGS.local_rank = 0
    FLAGS.multi_gpu  = "WORLD_SIZE" in os.environ and int(os.environ["WORLD_SIZE"]) > 1
    if FLAGS.multi_gpu:
        if "MASTER_ADDR" not in os.environ:
            os.environ["MASTER_ADDR"] = 'localhost'
        if "MASTER_PORT" not in os.environ:
            os.environ["MASTER_PORT"] = '23456'

        FLAGS.local_rank = int(os.environ["LOCAL_RANK"])
        torch.cuda.set_device(FLAGS.local_rank)
        torch.distributed.init_process_group(backend="nccl", init_method="env://")

    if FLAGS.config is not None:
        data = json.load(open(FLAGS.config, 'r'))
        for key in data:
            FLAGS.__dict__[key] = data[key]

    if FLAGS.display_res is None:
        FLAGS.display_res = FLAGS.train_res
    if FLAGS.out_dir is None:
        FLAGS.out_dir = 'out/cube_%d' % (FLAGS.train_res)
    else:
        FLAGS.out_dir = 'out/' + FLAGS.out_dir

    if FLAGS.local_rank == 0:
        print("Config / Flags:")
        print("---------")
        for key in FLAGS.__dict__.keys():
            print(key, FLAGS.__dict__[key])
        print("---------")

    os.makedirs(FLAGS.out_dir, exist_ok=True)

    glctx = dr.RasterizeGLContext()

    # ==============================================================================================
    #  Create data pipeline
    # ==============================================================================================
    if os.path.splitext(FLAGS.ref_mesh)[1] == '.obj':
        ref_mesh         = mesh.load_mesh(FLAGS.ref_mesh, FLAGS.mtl_override)
        dataset_train    = DatasetMesh(ref_mesh, glctx, RADIUS, FLAGS, validate=False)
        dataset_validate = DatasetMesh(ref_mesh, glctx, RADIUS, FLAGS, validate=True)
    elif os.path.isdir(FLAGS.ref_mesh):
        if os.path.isfile(os.path.join(FLAGS.ref_mesh, 'poses_bounds.npy')):
            dataset_train    = DatasetLLFF(FLAGS.ref_mesh, FLAGS, examples=(FLAGS.iter+1)*FLAGS.batch)
            dataset_validate = DatasetLLFF(FLAGS.ref_mesh, FLAGS)
        elif os.path.isfile(os.path.join(FLAGS.ref_mesh, 'transforms_train.json')):
            dataset_train    = DatasetNERF(os.path.join(FLAGS.ref_mesh, 'transforms_train.json'), FLAGS, examples=(FLAGS.iter+1)*FLAGS.batch)
            dataset_validate = DatasetNERF(os.path.join(FLAGS.ref_mesh, 'transforms_test.json'), FLAGS)

    # ==============================================================================================
    #  Create env light with trainable parameters
    # ==============================================================================================
    
    if FLAGS.learn_light:
        lgt = light.create_trainable_env_rnd(512, scale=0.0, bias=0.5)
    else:
        lgt = light.load_env(FLAGS.envmap, scale=FLAGS.env_scale)

    if FLAGS.base_mesh is None:
        # ==============================================================================================
        #  If no initial guess, use DMtets to create geometry
        # ==============================================================================================

        # Setup geometry for optimization
        geometry = DMTetGeometry(FLAGS.dmtet_grid, FLAGS.mesh_scale, FLAGS)

        # Setup textures, make initial guess from reference if possible
        mat = initial_guess_material(geometry, True, FLAGS)
    
        # Run optimization
        geometry, mat = optimize_mesh(glctx, geometry, mat, lgt, dataset_train, dataset_validate, 
                        FLAGS, pass_idx=0, pass_name="dmtet_pass1", optimize_light=FLAGS.learn_light)

        if FLAGS.local_rank == 0 and FLAGS.validate:
            validate(glctx, geometry, mat, lgt, dataset_validate, os.path.join(FLAGS.out_dir, "dmtet_validate"), FLAGS)

        # Create textured mesh from result
        base_mesh = xatlas_uvmap(glctx, geometry, mat, FLAGS)

        # Free temporaries / cached memory 
        torch.cuda.empty_cache()
        mat['kd_ks_normal'].cleanup()
        del mat['kd_ks_normal']

        lgt = lgt.clone()
        geometry = DLMesh(base_mesh, FLAGS)

        if FLAGS.local_rank == 0:
            # Dump mesh for debugging.
            os.makedirs(os.path.join(FLAGS.out_dir, "dmtet_mesh"), exist_ok=True)
            obj.write_obj(os.path.join(FLAGS.out_dir, "dmtet_mesh/"), base_mesh)
            light.save_env_map(os.path.join(FLAGS.out_dir, "dmtet_mesh/probe.hdr"), lgt)

        # ==============================================================================================
        #  Pass 2: Train with fixed topology (mesh)
        # ==============================================================================================
        geometry, mat = optimize_mesh(glctx, geometry, base_mesh.material, lgt, dataset_train, dataset_validate, FLAGS, 
                    pass_idx=1, pass_name="mesh_pass", warmup_iter=100, optimize_light=FLAGS.learn_light and not FLAGS.lock_light, 
                    optimize_geometry=not FLAGS.lock_pos)
    else:
        # ==============================================================================================
        #  Train with fixed topology (mesh)
        # ==============================================================================================

        # Load initial guess mesh from file
        base_mesh = mesh.load_mesh(FLAGS.base_mesh)
        geometry = DLMesh(base_mesh, FLAGS)
        
        mat = initial_guess_material(geometry, False, FLAGS, init_mat=base_mesh.material)

        geometry, mat = optimize_mesh(glctx, geometry, mat, lgt, dataset_train, dataset_validate, FLAGS, pass_idx=0, pass_name="mesh_pass", 
                                        warmup_iter=100, optimize_light=not FLAGS.lock_light, optimize_geometry=not FLAGS.lock_pos)

    # ==============================================================================================
    #  Validate
    # ==============================================================================================
    if FLAGS.validate and FLAGS.local_rank == 0:
        validate(glctx, geometry, mat, lgt, dataset_validate, os.path.join(FLAGS.out_dir, "validate"), FLAGS)

    # ==============================================================================================
    #  Dump output
    # ==============================================================================================
    if FLAGS.local_rank == 0:
        final_mesh = geometry.getMesh(mat)
        os.makedirs(os.path.join(FLAGS.out_dir, "mesh"), exist_ok=True)
        obj.write_obj(os.path.join(FLAGS.out_dir, "mesh/"), final_mesh)
        light.save_env_map(os.path.join(FLAGS.out_dir, "mesh/probe.hdr"), lgt)

#----------------------------------------------------------------------------