exp_runner.py

import os
import logging
import argparse
import numpy as np
import cv2 as cv
import trimesh
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from shutil import copyfile
from tqdm import tqdm
from pyhocon import ConfigFactory
from models.dataset import Dataset
from models.fields import RenderingNetwork, SDFNetwork, SingleVarianceNetwork, NeRF
from models.renderer import NeuSRenderer


class Runner:
    def __init__(self, conf_path, mode='train', case='CASE_NAME', is_continue=False):
        self.device = torch.device('cuda')

        # Configuration
        self.conf_path = conf_path
        f = open(self.conf_path)
        conf_text = f.read()
        conf_text = conf_text.replace('CASE_NAME', case)
        f.close()

        self.conf = ConfigFactory.parse_string(conf_text)
        self.conf['dataset.data_dir'] = self.conf['dataset.data_dir'].replace('CASE_NAME', case)
        self.base_exp_dir = self.conf['general.base_exp_dir']
        os.makedirs(self.base_exp_dir, exist_ok=True)
        self.dataset = Dataset(self.conf['dataset'])
        self.iter_step = 0

        # Training parameters
        self.end_iter = self.conf.get_int('train.end_iter')
        self.save_freq = self.conf.get_int('train.save_freq')
        self.report_freq = self.conf.get_int('train.report_freq')
        self.val_freq = self.conf.get_int('train.val_freq')
        self.val_mesh_freq = self.conf.get_int('train.val_mesh_freq')
        self.batch_size = self.conf.get_int('train.batch_size')
        self.validate_resolution_level = self.conf.get_int('train.validate_resolution_level')
        self.learning_rate = self.conf.get_float('train.learning_rate')
        self.learning_rate_alpha = self.conf.get_float('train.learning_rate_alpha')
        self.use_white_bkgd = self.conf.get_bool('train.use_white_bkgd')
        self.warm_up_end = self.conf.get_float('train.warm_up_end', default=0.0)
        self.anneal_end = self.conf.get_float('train.anneal_end', default=0.0)

        # Weights
        self.igr_weight = self.conf.get_float('train.igr_weight')
        self.mask_weight = self.conf.get_float('train.mask_weight')
        self.is_continue = is_continue
        self.mode = mode
        self.model_list = []
        self.writer = None        
        
        self.phase_delims = self.conf.get_list('train.phase_delim')
        self.bias_weights = self.conf.get_list('train.bias_weight')
        self.feat_weights = self.conf.get_list('train.feat_weight')
        self.depth_from_inside_only_s = self.conf.get_list('train.depth_from_inside_only')  
        self.object_mask_type = self.conf.get_string('train.object_mask_type')
        
        def get_param_in_phase(param_list, phase):
            if phase < self.phase_delims[0]: 
                return param_list[0]
            elif phase < self.phase_delims[1]:
                return param_list[1]
            else:
                return param_list[2]
        self.get_param_in_phase = get_param_in_phase

        # Networks
        params_to_train = []
        self.nerf_outside = NeRF(**self.conf['model.nerf']).to(self.device)
        self.sdf_network = SDFNetwork(**self.conf['model.sdf_network']).to(self.device)
        self.deviation_network = SingleVarianceNetwork(**self.conf['model.variance_network']).to(self.device)
        self.color_network = RenderingNetwork(**self.conf['model.rendering_network']).to(self.device)
        params_to_train += list(self.nerf_outside.parameters())
        params_to_train += list(self.sdf_network.parameters())
        params_to_train += list(self.deviation_network.parameters())
        params_to_train += list(self.color_network.parameters())

        self.optimizer = torch.optim.Adam(params_to_train, lr=self.learning_rate)

        self.renderer = NeuSRenderer(self.nerf_outside,
                                     self.sdf_network,
                                     self.deviation_network,
                                     self.color_network,
                                     **self.conf['model.neus_renderer'])

        # Load checkpoint
        latest_model_name = None
        if is_continue:
            model_list_raw = os.listdir(os.path.join(self.base_exp_dir, 'checkpoints'))
            model_list = []
            for model_name in model_list_raw:
                if model_name[-3:] == 'pth' and int(model_name[5:-4]) <= self.end_iter:
                    model_list.append(model_name)
            model_list.sort()
            latest_model_name = model_list[-1]

        if latest_model_name is not None:
            logging.info('Find checkpoint: {}'.format(latest_model_name))
            self.load_checkpoint(latest_model_name)

        # Backup codes and configs for debug
        if self.mode[:5] == 'train':
            self.file_backup()

    def train(self):
        self.writer = SummaryWriter(log_dir=os.path.join(self.base_exp_dir, 'logs'))
        self.update_learning_rate()
        res_step = self.end_iter - self.iter_step
        image_perm = self.get_image_perm()

        for iter_i in tqdm(range(res_step)):
            data ,sample = self.dataset.gen_random_rays_at(image_perm[self.iter_step % len(image_perm)], self.batch_size)

            rays_o, rays_d, true_rgb = data[:, :3], data[:, 3: 6], data[:, 6: 9]
            near, far = self.dataset.near_far_from_sphere(rays_o, rays_d)
            model_input = {}
            for attr in ['depth_cams','size', 'center', 'feat', 'feat_src','cam', 'src_cams', 'rays_d_norm']:
                model_input[attr] = sample[attr].cuda()
            for attr in ['H','W']:
                model_input[attr] = sample[attr]

            background_rgb = None
            if self.use_white_bkgd:
                background_rgb = torch.ones([1, 3])

            mask = torch.ones_like(true_rgb[...,0:1])
            mask_sum = mask.sum() + 1e-5
            train_phase = self.iter_step/self.end_iter
            render_out = self.renderer.render( rays_o,rays_d, near, far,
                                              background_rgb=background_rgb,
                                              cos_anneal_ratio=self.get_cos_anneal_ratio(),
                                              model_input = model_input,
                                              depth_from_inside_only = self.get_param_in_phase(self.depth_from_inside_only_s, train_phase),
                                              object_mask_type=self.object_mask_type)

            color_fine = render_out['color_fine']
            s_val = render_out['s_val']
            cdf_fine = render_out['cdf_fine']
            gradient_error = render_out['gradient_error']
            weight_max = render_out['weight_max']
            weight_sum = render_out['weight_sum']
            bias_loss = render_out['bias_loss']
            feat_loss = render_out['feat_loss']

            # Loss
            color_error = (color_fine - true_rgb) * mask
            color_fine_loss = F.l1_loss(color_error, torch.zeros_like(color_error), reduction='sum') / mask_sum
            psnr = 20.0 * torch.log10(1.0 / (((color_fine - true_rgb)**2 * mask).sum() / (mask_sum * 3.0)).sqrt())

            eikonal_loss = gradient_error

            mask_loss = F.binary_cross_entropy(weight_sum.clip(1e-3, 1.0 - 1e-3), mask)
            
            bias_loss = self.get_param_in_phase(self.bias_weights, train_phase) * bias_loss
            feat_loss = self.get_param_in_phase(self.feat_weights, train_phase) * feat_loss
            
            
            loss = color_fine_loss +\
                   eikonal_loss * self.igr_weight +\
                   mask_loss * self.mask_weight+\
                    bias_loss+\
                    feat_loss
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()

            self.iter_step += 1

            self.writer.add_scalar('Loss/loss', loss, self.iter_step)
            self.writer.add_scalar('Loss/color_loss', color_fine_loss, self.iter_step)
            self.writer.add_scalar('Loss/eikonal_loss', eikonal_loss, self.iter_step)
            self.writer.add_scalar('Loss/bias_loss', bias_loss, self.iter_step)
            self.writer.add_scalar('Loss/feat_loss', feat_loss, self.iter_step)
            self.writer.add_scalar('Statistics/s_val', s_val.mean(), self.iter_step)
            self.writer.add_scalar('Statistics/cdf', (cdf_fine[:, :1] * mask).sum() / mask_sum, self.iter_step)
            self.writer.add_scalar('Statistics/weight_max', (weight_max * mask).sum() / mask_sum, self.iter_step)
            self.writer.add_scalar('Statistics/psnr', psnr, self.iter_step)

            if self.iter_step % self.report_freq == 0:
                print(self.base_exp_dir)
                print('iter:{:8>d} loss = {} lr={}'.format(self.iter_step, loss, self.optimizer.param_groups[0]['lr']))

            if self.iter_step % self.save_freq == 0:
                self.save_checkpoint()

            if self.iter_step % self.val_freq == 0:
                self.validate_image()

            if self.iter_step % self.val_mesh_freq == 0:
                self.validate_mesh()

            self.update_learning_rate()

            if self.iter_step % len(image_perm) == 0:
                image_perm = self.get_image_perm()

    def get_image_perm(self):
        return torch.randperm(self.dataset.n_images)

    def get_cos_anneal_ratio(self):
        if self.anneal_end == 0.0:
            return 1.0
        else:
            return np.min([1.0, self.iter_step / self.anneal_end])

    def update_learning_rate(self):
        if self.iter_step < self.warm_up_end:
            learning_factor = self.iter_step / self.warm_up_end
        else:
            alpha = self.learning_rate_alpha
            progress = (self.iter_step - self.warm_up_end) / (self.end_iter - self.warm_up_end)
            learning_factor = (np.cos(np.pi * progress) + 1.0) * 0.5 * (1 - alpha) + alpha

        for g in self.optimizer.param_groups:
            g['lr'] = self.learning_rate * learning_factor

    def file_backup(self):
        dir_lis = self.conf['general.recording']
        os.makedirs(os.path.join(self.base_exp_dir, 'recording'), exist_ok=True)
        for dir_name in dir_lis:
            cur_dir = os.path.join(self.base_exp_dir, 'recording', dir_name)
            os.makedirs(cur_dir, exist_ok=True)
            files = os.listdir(dir_name)
            for f_name in files:
                if f_name[-3:] == '.py':
                    copyfile(os.path.join(dir_name, f_name), os.path.join(cur_dir, f_name))

        copyfile(self.conf_path, os.path.join(self.base_exp_dir, 'recording', 'config.conf'))

    def load_checkpoint(self, checkpoint_name):
        checkpoint = torch.load(os.path.join(self.base_exp_dir, 'checkpoints', checkpoint_name), map_location=self.device)
        self.nerf_outside.load_state_dict(checkpoint['nerf'])
        self.sdf_network.load_state_dict(checkpoint['sdf_network_fine'])
        self.deviation_network.load_state_dict(checkpoint['variance_network_fine'])
        self.color_network.load_state_dict(checkpoint['color_network_fine'])
        self.optimizer.load_state_dict(checkpoint['optimizer'])
        self.iter_step = checkpoint['iter_step']

        logging.info('End')

    def save_checkpoint(self):
        checkpoint = {
            'nerf': self.nerf_outside.state_dict(),
            'sdf_network_fine': self.sdf_network.state_dict(),
            'variance_network_fine': self.deviation_network.state_dict(),
            'color_network_fine': self.color_network.state_dict(),
            'optimizer': self.optimizer.state_dict(),
            'iter_step': self.iter_step,
        }

        os.makedirs(os.path.join(self.base_exp_dir, 'checkpoints'), exist_ok=True)
        torch.save(checkpoint, os.path.join(self.base_exp_dir, 'checkpoints', 'ckpt_{:0>6d}.pth'.format(self.iter_step)))

    def validate_image(self, idx=-1, resolution_level=-1):
        if idx < 0:
            idx = np.random.randint(self.dataset.n_images)

        print('Validate: iter: {}, camera: {}'.format(self.iter_step, idx))

        if resolution_level < 0:
            resolution_level = self.validate_resolution_level
        rays_o, rays_d = self.dataset.gen_rays_at(idx, resolution_level=resolution_level)
        H, W, _ = rays_o.shape
        rays_o = rays_o.reshape(-1, 3).split(self.batch_size)
        rays_d = rays_d.reshape(-1, 3).split(self.batch_size)

        out_rgb_fine = []
        out_normal_fine = []

        for rays_o_batch, rays_d_batch in zip(rays_o, rays_d):
            near, far = self.dataset.near_far_from_sphere(rays_o_batch, rays_d_batch)
            background_rgb = torch.ones([1, 3]) if self.use_white_bkgd else None

            render_out = self.renderer.render(rays_o_batch,
                                              rays_d_batch,
                                              near,
                                              far,
                                              cos_anneal_ratio=self.get_cos_anneal_ratio(),
                                              background_rgb=background_rgb)

            def feasible(key): return (key in render_out) and (render_out[key] is not None)

            if feasible('color_fine'):
                out_rgb_fine.append(render_out['color_fine'].detach().cpu().numpy())
            if feasible('gradients') and feasible('weights'):
                n_samples = self.renderer.n_samples + self.renderer.n_importance
                normals = render_out['gradients'] * render_out['weights'][:, :n_samples, None]
                if feasible('inside_sphere'):
                    normals = normals * render_out['inside_sphere'][..., None]
                normals = normals.sum(dim=1).detach().cpu().numpy()
                out_normal_fine.append(normals)
            del render_out

        img_fine = None
        if len(out_rgb_fine) > 0:
            img_fine = (np.concatenate(out_rgb_fine, axis=0).reshape([H, W, 3, -1]) * 256).clip(0, 255)

        normal_img = None
        if len(out_normal_fine) > 0:
            normal_img = np.concatenate(out_normal_fine, axis=0)
            rot = np.linalg.inv(self.dataset.pose_all[idx, :3, :3].detach().cpu().numpy())
            normal_img = (np.matmul(rot[None, :, :], normal_img[:, :, None])
                          .reshape([H, W, 3, -1]) * 128 + 128).clip(0, 255)

        os.makedirs(os.path.join(self.base_exp_dir, 'validations_fine'), exist_ok=True)
        os.makedirs(os.path.join(self.base_exp_dir, 'normals'), exist_ok=True)

        for i in range(img_fine.shape[-1]):
            if len(out_rgb_fine) > 0:
                cv.imwrite(os.path.join(self.base_exp_dir,
                                        'validations_fine',
                                        '{:0>8d}_{}_{}.png'.format(self.iter_step, i, idx)),
                           np.concatenate([img_fine[..., i],
                                           self.dataset.image_at(idx, resolution_level=resolution_level)]))
            if len(out_normal_fine) > 0:
                cv.imwrite(os.path.join(self.base_exp_dir,
                                        'normals',
                                        '{:0>8d}_{}_{}.png'.format(self.iter_step, i, idx)),
                           normal_img[..., i])

    def rendering_image(self, idx=-1, resolution_level=-1):
        if idx < 0:
            idx = np.random.randint(self.dataset.n_images)

        if resolution_level < 0:
            resolution_level = self.validate_resolution_level
        rays_o, rays_d = self.dataset.gen_rays_at(idx, resolution_level=resolution_level)
        H, W, _ = rays_o.shape
        rays_o = rays_o.reshape(-1, 3).split(self.batch_size)
        rays_d = rays_d.reshape(-1, 3).split(self.batch_size)

        out_rgb_fine = []

        for rays_o_batch, rays_d_batch in zip(rays_o, rays_d):
            near, far = self.dataset.near_far_from_sphere(rays_o_batch, rays_d_batch)
            background_rgb = torch.ones([1, 3]) if self.use_white_bkgd else None

            render_out = self.renderer.render(rays_o_batch,
                                              rays_d_batch,
                                              near,
                                              far,
                                              cos_anneal_ratio=self.get_cos_anneal_ratio(),
                                              background_rgb=background_rgb)

            def feasible(key): return (key in render_out) and (render_out[key] is not None)

            if feasible('color_fine'):
                out_rgb_fine.append(render_out['color_fine'].detach().cpu().numpy())

        img_fine = None
        if len(out_rgb_fine) > 0:
            img_fine = (np.concatenate(out_rgb_fine, axis=0).reshape([H, W, 3, -1]) * 256).clip(0, 255)


        os.makedirs(os.path.join(self.base_exp_dir, 'rendering_image'), exist_ok=True)


        for i in range(img_fine.shape[-1]):
            if len(out_rgb_fine) > 0:
                cv.imwrite(os.path.join(self.base_exp_dir,
                                        'rendering_image',
                                        '{}.png'.format(idx)),
                                        img_fine[..., i]
                                           )
                
    def output_rendering_image (self, resolution = -1 ):
        for i in range (self.dataset.n_images):
            self.rendering_image(idx= i , resolution_level=resolution)


    def render_novel_image(self, idx_0, idx_1, ratio, resolution_level):
        """
        Interpolate view between two cameras.
        """
        rays_o, rays_d = self.dataset.gen_rays_between(idx_0, idx_1, ratio, resolution_level=resolution_level)
        H, W, _ = rays_o.shape
        rays_o = rays_o.reshape(-1, 3).split(self.batch_size)
        rays_d = rays_d.reshape(-1, 3).split(self.batch_size)

        out_rgb_fine = []
        for rays_o_batch, rays_d_batch in zip(rays_o, rays_d):
            near, far = self.dataset.near_far_from_sphere(rays_o_batch, rays_d_batch)
            background_rgb = torch.ones([1, 3]) if self.use_white_bkgd else None

            render_out = self.renderer.render(rays_o_batch,
                                              rays_d_batch,
                                              near,
                                              far,
                                              cos_anneal_ratio=self.get_cos_anneal_ratio(),
                                              background_rgb=background_rgb)

            out_rgb_fine.append(render_out['color_fine'].detach().cpu().numpy())

            del render_out

        img_fine = (np.concatenate(out_rgb_fine, axis=0).reshape([H, W, 3]) * 256).clip(0, 255).astype(np.uint8)
        return img_fine

    def validate_mesh(self, world_space=False, resolution=64, threshold=0.0):
        bound_min = torch.tensor(self.dataset.object_bbox_min, dtype=torch.float32)
        bound_max = torch.tensor(self.dataset.object_bbox_max, dtype=torch.float32)

        vertices, triangles =\
            self.renderer.extract_geometry(bound_min, bound_max, resolution=resolution, threshold=threshold)
        os.makedirs(os.path.join(self.base_exp_dir, 'meshes'), exist_ok=True)

        if world_space:
            vertices = vertices * self.dataset.scale_mats_np[0][0, 0] + self.dataset.scale_mats_np[0][:3, 3][None]

        mesh = trimesh.Trimesh(vertices, triangles)
        mesh.export(os.path.join(self.base_exp_dir, 'meshes', '{:0>8d}.ply'.format(self.iter_step)))

        logging.info('End')

    def interpolate_view(self, img_idx_0, img_idx_1):
        images = []
        n_frames = 60
        for i in range(n_frames):
            print(i)
            images.append(self.render_novel_image(img_idx_0,
                                                  img_idx_1,
                                                  np.sin(((i / n_frames) - 0.5) * np.pi) * 0.5 + 0.5,
                          resolution_level=4))
        for i in range(n_frames):
            images.append(images[n_frames - i - 1])

        fourcc = cv.VideoWriter_fourcc(*'mp4v')
        video_dir = os.path.join(self.base_exp_dir, 'render')
        os.makedirs(video_dir, exist_ok=True)
        h, w, _ = images[0].shape
        writer = cv.VideoWriter(os.path.join(video_dir,
                                             '{:0>8d}_{}_{}.mp4'.format(self.iter_step, img_idx_0, img_idx_1)),
                                fourcc, 30, (w, h))

        for image in images:
            writer.write(image)

        writer.release()


if __name__ == '__main__':
    torch.set_default_tensor_type('torch.cuda.FloatTensor')

    FORMAT = "[%(filename)s:%(lineno)s - %(funcName)20s() ] %(message)s"
    logging.basicConfig(level=logging.DEBUG, format=FORMAT)

    parser = argparse.ArgumentParser()
    parser.add_argument('--conf', type=str, default='./conf')
    parser.add_argument('--mode', type=str, default='train')
    parser.add_argument('--mcube_threshold', type=float, default=0.0)
    parser.add_argument('--volume_resolution', type=int, default=512)
    parser.add_argument('--is_continue', default=False, action="store_true")
    parser.add_argument('--gpu', type=int, default=0)
    parser.add_argument('--case', type=str, default='')
    parser.add_argument('--world_space', default=False, action="store_true")    

    args = parser.parse_args()

    torch.cuda.set_device(args.gpu)
    runner = Runner(args.conf, args.mode, args.case, args.is_continue)

    if args.mode == 'train':
        runner.train()
        runner.validate_mesh(world_space=False, resolution=args.volume_resolution, threshold=args.mcube_threshold)
    elif args.mode == 'validate_mesh':
        runner.validate_mesh(world_space=args.world_space, resolution=args.volume_resolution, threshold=args.mcube_threshold)
    elif args.mode == 'render_image':
        runner.output_rendering_image(resolution=1)
    elif args.mode.startswith('interpolate'):  # Interpolate views given two image indices
        _, img_idx_0, img_idx_1 = args.mode.split('_')
        img_idx_0 = int(img_idx_0)
        img_idx_1 = int(img_idx_1)
        runner.interpolate_view(img_idx_0, img_idx_1)