【代码详解】nerf-pytorch代码逐行分析

前言

要想看懂instant-ngp的cuda代码，需要先对NeRF系列有足够深入的了解，原始的NeRF版本是基于tensorflow的，今天读的是MIT博士生Yen-Chen Lin实现的pytorch版本的代码。
代码链接：https://github.com/yenchenlin/nerf-pytorch
因为代码量比较大，所以我们先使用一个思维导图对项目逻辑进行梳理，然后逐个文件解析。为了保持思路连贯，我们会一次贴上整个函数的内容并逐行注释，然后贴相关的公式和示意图到代码段的下方。

run_nerf.py

一切都从这个文件开始，让我们先来看看有哪些参数需要设置。

config_parser()

先是一些基本参数

    # 生成config.txt文件
    parser.add_argument('--config', is_config_file=True, 
                        help='config file path')
    # 指定实验名称
    parser.add_argument("--expname", type=str, 
                        help='experiment name')
    # 指定输出目录
    parser.add_argument("--basedir", type=str, default='./logs/', 
                        help='where to store ckpts and logs')
    # 指定数据目录
    parser.add_argument("--datadir", type=str, default='./data/llff/fern', 
                        help='input data directory')

然后是一些训练相关的参数

    # training options
    # 设置网络的深度，即网络的层数
    parser.add_argument("--netdepth", type=int, default=8, 
                        help='layers in network')
    # 设置网络的宽度，即每一层神经元的个数
    parser.add_argument("--netwidth", type=int, default=256, 
                        help='channels per layer')
    parser.add_argument("--netdepth_fine", type=int, default=8, 
                        help='layers in fine network')
    parser.add_argument("--netwidth_fine", type=int, default=256, 
                        help='channels per layer in fine network')
    # batch size，光束的数量
    parser.add_argument("--N_rand", type=int, default=32*32*4, 
                        help='batch size (number of random rays per gradient step)')
    # 学习率
    parser.add_argument("--lrate", type=float, default=5e-4, 
                        help='learning rate')
    # 指数学习率衰减
    parser.add_argument("--lrate_decay", type=int, default=250, 
                        help='exponential learning rate decay (in 1000 steps)')
    # 并行处理的光线数量，如果溢出则减少
    parser.add_argument("--chunk", type=int, default=1024*32, 
                        help='number of rays processed in parallel, decrease if running out of memory')
    # 并行发送的点数
    parser.add_argument("--netchunk", type=int, default=1024*64, 
                        help='number of pts sent through network in parallel, decrease if running out of memory')
    # 一次只能从一张图片中获取随机光线
    parser.add_argument("--no_batching", action='store_true', 
                        help='only take random rays from 1 image at a time')
    # 不要从保存的模型中加载权重
    parser.add_argument("--no_reload", action='store_true', 
                        help='do not reload weights from saved ckpt')
    # 为粗网络重新加载特定权重
    parser.add_argument("--ft_path", type=str, default=None, 
                        help='specific weights npy file to reload for coarse network')

然后是一些渲染时的参数

    # rendering options
    # 每条射线的粗样本数
    parser.add_argument("--N_samples", type=int, default=64, 
                        help='number of coarse samples per ray')
    # 每条射线附加的细样本数
    parser.add_argument("--N_importance", type=int, default=0,
                        help='number of additional fine samples per ray')
    # 抖动
    parser.add_argument("--perturb", type=float, default=1.,
                        help='set to 0. for no jitter, 1. for jitter')
    parser.add_argument("--use_viewdirs", action='store_true', 
                        help='use full 5D input instead of 3D')
    # 默认位置编码
    parser.add_argument("--i_embed", type=int, default=0, 
                        help='set 0 for default positional encoding, -1 for none')
    # 多分辨率
    parser.add_argument("--multires", type=int, default=10, 
                        help='log2 of max freq for positional encoding (3D location)')
    # 2D方向的多分辨率
    parser.add_argument("--multires_views", type=int, default=4, 
                        help='log2 of max freq for positional encoding (2D direction)')
    # 噪音方差
    parser.add_argument("--raw_noise_std", type=float, default=0., 
                        help='std dev of noise added to regularize sigma_a output, 1e0 recommended')

    # 不要优化，重新加载权重和渲染render_poses路径
    parser.add_argument("--render_only", action='store_true', 
                        help='do not optimize, reload weights and render out render_poses path')
    # 渲染测试集而不是render_poses路径
    parser.add_argument("--render_test", action='store_true', 
                        help='render the test set instead of render_poses path')
    # 下采样因子以加快渲染速度，设置为 4 或 8 用于快速预览
    parser.add_argument("--render_factor", type=int, default=0, 
                        help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')

还有一些参数

    # training options
    parser.add_argument("--precrop_iters", type=int, default=0,
                        help='number of steps to train on central crops')
    parser.add_argument("--precrop_frac", type=float,
                        default=.5, help='fraction of img taken for central crops') 

    # dataset options
    parser.add_argument("--dataset_type", type=str, default='llff', 
                        help='options: llff / blender / deepvoxels')
    # # 将从测试/验证集中加载 1/N 图像，这对于像 deepvoxels 这样的大型数据集很有用
    parser.add_argument("--testskip", type=int, default=8, 
                        help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')

    ## deepvoxels flags
    parser.add_argument("--shape", type=str, default='greek', 
                        help='options : armchair / cube / greek / vase')

    ## blender flags
    parser.add_argument("--white_bkgd", action='store_true', 
                        help='set to render synthetic data on a white bkgd (always use for dvoxels)')
    parser.add_argument("--half_res", action='store_true', 
                        help='load blender synthetic data at 400x400 instead of 800x800')

    ## llff flags
    # LLFF下采样因子
    parser.add_argument("--factor", type=int, default=8, 
                        help='downsample factor for LLFF images')
    parser.add_argument("--no_ndc", action='store_true', 
                        help='do not use normalized device coordinates (set for non-forward facing scenes)')
    parser.add_argument("--lindisp", action='store_true', 
                        help='sampling linearly in disparity rather than depth')
    parser.add_argument("--spherify", action='store_true', 
                        help='set for spherical 360 scenes')
    parser.add_argument("--llffhold", type=int, default=8, 
                        help='will take every 1/N images as LLFF test set, paper uses 8')

    # logging/saving options
    parser.add_argument("--i_print",   type=int, default=100, 
                        help='frequency of console printout and metric loggin')
    parser.add_argument("--i_img",     type=int, default=500, 
                        help='frequency of tensorboard image logging')
    parser.add_argument("--i_weights", type=int, default=10000, 
                        help='frequency of weight ckpt saving')
    parser.add_argument("--i_testset", type=int, default=50000, 
                        help='frequency of testset saving')
    parser.add_argument("--i_video",   type=int, default=50000, 
                        help='frequency of render_poses video saving')

train()

训练过程的控制。开始训练，先把5D输入进行编码，然后交给MLP得到4D的数据（颜色和体素的密度），然后进行体渲染得到图片，再和真值计算L2 loss。

def train():

    parser = config_parser()
    args = parser.parse_args()

    # Load data
    K = None
    if args.dataset_type == 'llff':
        # shape: images[20,378,504,3] poses[20,3,5] render_poses[120,3,5]
        images, poses, bds, render_poses, i_test = load_llff_data(args.datadir, args.factor,
                                                                  recenter=True, bd_factor=.75,
                                                                  spherify=args.spherify)
        # hwf=[378,504,focal] poses每个batch的每一行最后一个元素拿出来
        hwf = poses[0,:3,-1]
        # shape: poses [20,3,4] hwf给出去之后把每一行的第5个元素删掉
        poses = poses[:,:3,:4]
        print('Loaded llff', images.shape, render_poses.shape, hwf, args.datadir)
        if not isinstance(i_test, list):
            i_test = [i_test]

        if args.llffhold > 0:
            print('Auto LLFF holdout,', args.llffhold)
            i_test = np.arange(images.shape[0])[::args.llffhold]

        # 验证集和测试集相同
        i_val = i_test
        # 剩下的部分当作训练集
        i_train = np.array([i for i in np.arange(int(images.shape[0])) if
                        (i not in i_test and i not in i_val)])

        print('DEFINING BOUNDS')
        # 定义边界值
        if args.no_ndc:
            near = np.ndarray.min(bds) * .9
            far = np.ndarray.max(bds) * 1.
            
        else:
        # 没说就是0-1
            near = 0.
            far = 1.
        print('NEAR FAR', near, far)

    elif args.dataset_type == 'blender':
        images, poses, render_poses, hwf, i_split = load_blender_data(args.datadir, args.half_res, args.testskip)
        print('Loaded blender', images.shape, render_poses.shape, hwf, args.datadir)
        i_train, i_val, i_test = i_split

        near = 2.
        far = 6.

        if args.white_bkgd:
            images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
        else:
            images = images[...,:3]

    elif args.dataset_type == 'LINEMOD':
        images, poses, render_poses, hwf, K, i_split, near, far = load_LINEMOD_data(args.datadir, args.half_res, args.testskip)
        print(f'Loaded LINEMOD, images shape: {images.shape}, hwf: {hwf}, K: {K}')
        print(f'[CHECK HERE] near: {near}, far: {far}.')
        i_train, i_val, i_test = i_split

        if args.white_bkgd:
            images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
        else:
            images = images[...,:3]

    elif args.dataset_type == 'deepvoxels':

        images, poses, render_poses, hwf, i_split = load_dv_data(scene=args.shape,
                                                                 basedir=args.datadir,
                                                                 testskip=args.testskip)

        print('Loaded deepvoxels', images.shape, render_poses.shape, hwf, args.datadir)
        i_train, i_val, i_test = i_split

        hemi_R = np.mean(np.linalg.norm(poses[:,:3,-1], axis=-1))
        near = hemi_R-1.
        far = hemi_R+1.

    else:
        print('Unknown dataset type', args.dataset_type, 'exiting')
        return

    # Cast intrinsics to right types
    H, W, focal = hwf
    H, W = int(H), int(W)
    hwf = [H, W, focal]

    if K is None:
        K = np.array([
            [focal, 0, 0.5*W],
            [0, focal, 0.5*H],
            [0, 0, 1]
        ])

    if args.render_test:
        render_poses = np.array(poses[i_test])

    # Create log dir and copy the config file
    basedir = args.basedir
    expname = args.expname
    os.makedirs(os.path.join(basedir, expname), exist_ok=True)
    f = os.path.join(basedir, expname, 'args.txt')
    with open(f, 'w') as file:
        # 把参数统一放到./logs/expname/args.txt
        for arg in sorted(vars(args)):
            attr = getattr(args, arg)
            file.write('{} = {}\n'.format(arg, attr))
    if args.config is not None:
        f = os.path.join(basedir, expname, 'config.txt')
        with open(f, 'w') as file:
            file.write(open(args.config, 'r').read())

    # Create nerf model
    # 创建模型
    render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(args)
    global_step = start

    bds_dict = {
        'near' : near,
        'far' : far,
    }
    # 本来都是dict类型，都有9个元素，加了bds之后就是11个元素了
    render_kwargs_train.update(bds_dict)
    render_kwargs_test.update(bds_dict)

    # Move testing data to GPU
    render_poses = torch.Tensor(render_poses).to(device)

    # Short circuit if only rendering out from trained model
    # 只渲染并生成视频
    if args.render_only:
        print('RENDER ONLY')
        with torch.no_grad():
            if args.render_test:
                # render_test switches to test poses
                images = images[i_test]
            else:
                # Default is smoother render_poses path
                images = None

            testsavedir = os.path.join(basedir, expname, 'renderonly_{}_{:06d}'.format('test' if args.render_test else 'path', start))
            os.makedirs(testsavedir, exist_ok=True)
            print('test poses shape', render_poses.shape)

            rgbs, _ = render_path(render_poses, hwf, K, args.chunk, render_kwargs_test, gt_imgs=images, savedir=testsavedir, render_factor=args.render_factor)
            print('Done rendering', testsavedir)
            imageio.mimwrite(os.path.join(testsavedir, 'video.mp4'), to8b(rgbs), fps=30, quality=8)

            return

    # Prepare raybatch tensor if batching random rays
    N_rand = args.N_rand # 4096
    use_batching = not args.no_batching
    if use_batching:
        # For random ray batching
        print('get rays')
        # 获取光束, rays shape:[20,2,378,504,3]
        rays = np.stack([get_rays_np(H, W, K, p) for p in poses[:,:3,:4]], 0) # [N, ro+rd, H, W, 3]
        print('done, concats')
        # 沿axis=1拼接,rayss_rgb shape:[20,3,378,504,3]
        rays_rgb = np.concatenate([rays, images[:,None]], 1) # [N, ro+rd+rgb, H, W, 3]
        # 改变shape,rays_rgb shape:[20,378,504,3,3]
        rays_rgb = np.transpose(rays_rgb, [0,2,3,1,4]) # [N, H, W, ro+rd+rgb, 3]
        # rays_rgb shape:[N-测试样本数目=17,378,504,3,3]
        rays_rgb = np.stack([rays_rgb[i] for i in i_train], 0) # train images only
        # 得到了(N-测试样本数目)*H*W个光束,rays_rgb shape:[(N-test)*H*W,3,3]
        rays_rgb = np.reshape(rays_rgb, [-1,3,3]) # [(N-test)*H*W, ro+rd+rgb, 3]
        rays_rgb = rays_rgb.astype(np.float32)
        print('shuffle rays')
        # 打乱这个光束的顺序
        np.random.shuffle(rays_rgb)

        print('done')
        i_batch = 0

    # Move training data to GPU
    if use_batching:
        images = torch.Tensor(images).to(device)
    poses = torch.Tensor(poses).to(device)
    if use_batching:
        rays_rgb = torch.Tensor(rays_rgb).to(device)


    N_iters = 200000 + 1
    print('Begin')
    print('TRAIN views are', i_train)
    print('TEST views are', i_test)
    print('VAL views are', i_val)

    # Summary writers
    # writer = SummaryWriter(os.path.join(basedir, 'summaries', expname))
    
    # 默认训练200000次
    start = start + 1
    for i in trange(start, N_iters):
        time0 = time.time()

        # Sample random ray batch
        if use_batching:
            # Random over all images
            # 取一个batch, batch shape：[4096,3,3]
            batch = rays_rgb[i_batch:i_batch+N_rand] # [B, 2+1, 3*?]
            # 转换0维和1维的位置[ro+rd+rgb,4096,3]
            batch = torch.transpose(batch, 0, 1)
            # shape: batch_rays shape[ro+rd,4096,3] target_s[4096,3]对应的是rgb
            batch_rays, target_s = batch[:2], batch[2]

            i_batch += N_rand
            # 如果所有样本都遍历过了则打乱数据
            if i_batch >= rays_rgb.shape[0]:
                print("Shuffle data after an epoch!")
                rand_idx = torch.randperm(rays_rgb.shape[0])
                rays_rgb = rays_rgb[rand_idx]
                i_batch = 0

        else:
            # Random from one image
            img_i = np.random.choice(i_train)
            target = images[img_i]
            target = torch.Tensor(target).to(device)
            pose = poses[img_i, :3,:4]

            if N_rand is not None:
                rays_o, rays_d = get_rays(H, W, K, torch.Tensor(pose))  # (H, W, 3), (H, W, 3)

                if i < args.precrop_iters:
                    dH = int(H//2 * args.precrop_frac)
                    dW = int(W//2 * args.precrop_frac)
                    coords = torch.stack(
                        torch.meshgrid(
                            torch.linspace(H//2 - dH, H//2 + dH - 1, 2*dH), 
                            torch.linspace(W//2 - dW, W//2 + dW - 1, 2*dW)
                        ), -1)
                    if i == start:
                        print(f"[Config] Center cropping of size {2*dH} x {2*dW} is enabled until iter {args.precrop_iters}")                
                else:
                    coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1)  # (H, W, 2)

                coords = torch.reshape(coords, [-1,2])  # (H * W, 2)
                select_inds = np.random.choice(coords.shape[0], size=[N_rand], replace=False)  # (N_rand,)
                select_coords = coords[select_inds].long()  # (N_rand, 2)
                rays_o = rays_o[select_coords[:, 0], select_coords[:, 1]]  # (N_rand, 3)
                rays_d = rays_d[select_coords[:, 0], select_coords[:, 1]]  # (N_rand, 3)
                batch_rays = torch.stack([rays_o, rays_d], 0)
                target_s = target[select_coords[:, 0], select_coords[:, 1]]  # (N_rand, 3)

        #####  Core optimization loop  #####
        # chunk=4096,batch_rays[2,4096,3]
        # 返回渲染出的一个batch的rgb，disp（视差图），acc（不透明度）和extras（其他信息）
        # rgb shape [4096, 3]刚好可以和target_s 对应上
        # disp shape 4096，对应4096个光束
        # acc shape 4096， 对应4096个光束
        # extras 是一个dict，含有5个元素 shape:[4096,64,4]
        rgb, disp, acc, extras = render(H, W, K, chunk=args.chunk, rays=batch_rays,
                                                verbose=i < 10, retraw=True,
                                                **render_kwargs_train)

        optimizer.zero_grad()
        # 求RGB的MSE img_loss shape:[20,378,504,3]
        img_loss = img2mse(rgb, target_s)
        # trans shape:[4096,64]
        trans = extras['raw'][...,-1]
        loss = img_loss
        # 计算PSNR shape:[1]
        psnr = mse2psnr(img_loss)

        # 在extra里面的一个元素，求损失并加到整体损失上
        if 'rgb0' in extras:
            img_loss0 = img2mse(extras['rgb0'], target_s)
            loss = loss + img_loss0
            psnr0 = mse2psnr(img_loss0)

        loss.backward()
        optimizer.step()

        # NOTE: IMPORTANT!
        ###   update learning rate   ###
        decay_rate = 0.1
        decay_steps = args.lrate_decay * 1000
        new_lrate = args.lrate * (decay_rate ** (global_step / decay_steps))
        for param_group in optimizer.param_groups:
            param_group['lr'] = new_lrate
        ################################

        dt = time.time()-time0
        # print(f"Step: {global_step}, Loss: {loss}, Time: {dt}")
        #####           end            #####

        # Rest is logging
        # 保存ckpt
        if i%args.i_weights==0:
            path = os.path.join(basedir, expname, '{:06d}.tar'.format(i))
            torch.save({
                'global_step': global_step,
                'network_fn_state_dict': render_kwargs_train['network_fn'].state_dict(),
                'network_fine_state_dict': render_kwargs_train['network_fine'].state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
            }, path)
            print('Saved checkpoints at', path)

        # 输出mp4视频
        if i%args.i_video==0 and i > 0:
            # Turn on testing mode
            # reder_poses用来合成视频
            with torch.no_grad():
                rgbs, disps = render_path(render_poses, hwf, K, args.chunk, render_kwargs_test)
            print('Done, saving', rgbs.shape, disps.shape)
            moviebase = os.path.join(basedir, expname, '{}_spiral_{:06d}_'.format(expname, i))
            imageio.mimwrite(moviebase + 'rgb.mp4', to8b(rgbs), fps=30, quality=8)
            imageio.mimwrite(moviebase + 'disp.mp4', to8b(disps / np.max(disps)), fps=30, quality=8)

            # if args.use_viewdirs:
            #     render_kwargs_test['c2w_staticcam'] = render_poses[0][:3,:4]
            #     with torch.no_grad():
            #         rgbs_still, _ = render_path(render_poses, hwf, args.chunk, render_kwargs_test)
            #     render_kwargs_test['c2w_staticcam'] = None
            #     imageio.mimwrite(moviebase + 'rgb_still.mp4', to8b(rgbs_still), fps=30, quality=8)

        # 保存测试数据集
        if i%args.i_testset==0 and i > 0:
            testsavedir = os.path.join(basedir, expname, 'testset_{:06d}'.format(i))
            os.makedirs(testsavedir, exist_ok=True)
            print('test poses shape', poses[i_test].shape)
            with torch.no_grad():
                render_path(torch.Tensor(poses[i_test]).to(device), hwf, K, args.chunk, render_kwargs_test, gt_imgs=images[i_test], savedir=testsavedir)
            print('Saved test set')


    
        if i%args.i_print==0:
            tqdm.write(f"[TRAIN] Iter: {i} Loss: {loss.item()}  PSNR: {psnr.item()}")
        """
            print(expname, i, psnr.numpy(), loss.numpy(), global_step.numpy())
            print('iter time {:.05f}'.format(dt))

            with tf.contrib.summary.record_summaries_every_n_global_steps(args.i_print):
                tf.contrib.summary.scalar('loss', loss)
                tf.contrib.summary.scalar('psnr', psnr)
                tf.contrib.summary.histogram('tran', trans)
                if args.N_importance > 0:
                    tf.contrib.summary.scalar('psnr0', psnr0)


            if i%args.i_img==0:

                # Log a rendered validation view to Tensorboard
                img_i=np.random.choice(i_val)
                target = images[img_i]
                pose = poses[img_i, :3,:4]
                with torch.no_grad():
                    rgb, disp, acc, extras = render(H, W, focal, chunk=args.chunk, c2w=pose,
                                                        **render_kwargs_test)

                psnr = mse2psnr(img2mse(rgb, target))

                with tf.contrib.summary.record_summaries_every_n_global_steps(args.i_img):

                    tf.contrib.summary.image('rgb', to8b(rgb)[tf.newaxis])
                    tf.contrib.summary.image('disp', disp[tf.newaxis,...,tf.newaxis])
                    tf.contrib.summary.image('acc', acc[tf.newaxis,...,tf.newaxis])

                    tf.contrib.summary.scalar('psnr_holdout', psnr)
                    tf.contrib.summary.image('rgb_holdout', target[tf.newaxis])


                if args.N_importance > 0:

                    with tf.contrib.summary.record_summaries_every_n_global_steps(args.i_img):
                        tf.contrib.summary.image('rgb0', to8b(extras['rgb0'])[tf.newaxis])
                        tf.contrib.summary.image('disp0', extras['disp0'][tf.newaxis,...,tf.newaxis])
                        tf.contrib.summary.image('z_std', extras['z_std'][tf.newaxis,...,tf.newaxis])
        """

        global_step += 1

梳理完train，我们来重点看一下train当中调用过的几个函数

create_nerf()

先调用get_embedder获得一个对应的embedding函数，然后构建NeRF模型

def create_nerf(args):
    """Instantiate NeRF's MLP model.
    """
    embed_fn, input_ch = get_embedder(args.multires, args.i_embed)

    input_ch_views = 0
    embeddirs_fn = None
    if args.use_viewdirs:
        embeddirs_fn, input_ch_views = get_embedder(args.multires_views, args.i_embed)
    output_ch = 5 if args.N_importance > 0 else 4
    skips = [4]
    # 构建模型
    model = NeRF(D=args.netdepth, W=args.netwidth,
                 input_ch=input_ch, output_ch=output_ch, skips=skips,
                 input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs).to(device)
    # 梯度
    grad_vars = list(model.parameters())

    model_fine = None
    if args.N_importance > 0:
        # 需要精细网络
        model_fine = NeRF(D=args.netdepth_fine, W=args.netwidth_fine,
                          input_ch=input_ch, output_ch=output_ch, skips=skips,
                          input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs).to(device)
        grad_vars += list(model_fine.parameters())

    network_query_fn = lambda inputs, viewdirs, network_fn : run_network(inputs, viewdirs, network_fn,
                                                                embed_fn=embed_fn,
                                                                embeddirs_fn=embeddirs_fn,
                                                                netchunk=args.netchunk)

    # Create optimizer
    optimizer = torch.optim.Adam(params=grad_vars, lr=args.lrate, betas=(0.9, 0.999))

    start = 0
    basedir = args.basedir
    expname = args.expname

    ##########################

    # Load checkpoints
    if args.ft_path is not None and args.ft_path!='None':
        ckpts = [args.ft_path]
    else:
        ckpts = [os.path.join(basedir, expname, f) for f in sorted(os.listdir(os.path.join(basedir, expname))) if 'tar' in f]

    print('Found ckpts', ckpts)
    if len(ckpts) > 0 and not args.no_reload:
        ckpt_path = ckpts[-1]
        print('Reloading from', ckpt_path)
        ckpt = torch.load(ckpt_path)

        start = ckpt['global_step']
        optimizer.load_state_dict(ckpt['optimizer_state_dict'])

        # Load model
        model.load_state_dict(ckpt['network_fn_state_dict'])
        if model_fine is not None:
            model_fine.load_state_dict(ckpt['network_fine_state_dict'])

    ##########################

    # 加载模型
    render_kwargs_train = {
        'network_query_fn' : network_query_fn,
        'perturb' : args.perturb,
        'N_importance' : args.N_importance,
        'network_fine' : model_fine,
        'N_samples' : args.N_samples,
        'network_fn' : model,
        'use_viewdirs' : args.use_viewdirs,
        'white_bkgd' : args.white_bkgd,
        'raw_noise_std' : args.raw_noise_std,
    }

    # NDC only good for LLFF-style forward facing data
    if args.dataset_type != 'llff' or args.no_ndc:
        print('Not ndc!')
        render_kwargs_train['ndc'] = False
        render_kwargs_train['lindisp'] = args.lindisp

    render_kwargs_test = {k : render_kwargs_train[k] for k in render_kwargs_train}
    render_kwargs_test['perturb'] = False
    render_kwargs_test['raw_noise_std'] = 0.

    return render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer

render()

接下来我们看一下如何渲染，render函数返回的是光束对应的rgb图、视差图、不透明度，以及raw

def render(H, W, K, chunk=1024*32, rays=None, c2w=None, ndc=True,
                  near=0., far=1.,
                  use_viewdirs=False, c2w_staticcam=None,
                  **kwargs):
    """Render rays
    Args:
      H: int. Height of image in pixels.
      W: int. Width of image in pixels.
      focal: float. Focal length of pinhole camera.
      chunk: int. Maximum number of rays to process simultaneously. Used to
        control maximum memory usage. Does not affect final results.
      rays: array of shape [2, batch_size, 3]. Ray origin and direction for
        each example in batch.
      c2w: array of shape [3, 4]. Camera-to-world transformation matrix.
      ndc: bool. If True, represent ray origin, direction in NDC coordinates.
      near: float or array of shape [batch_size]. Nearest distance for a ray.
      far: float or array of shape [batch_size]. Farthest distance for a ray.
      use_viewdirs: bool. If True, use viewing direction of a point in space in model.
      c2w_staticcam: array of shape [3, 4]. If not None, use this transformation matrix for 
       camera while using other c2w argument for viewing directions.
    Returns:
      rgb_map: [batch_size, 3]. Predicted RGB values for rays.
      disp_map: [batch_size]. Disparity map. Inverse of depth.
      acc_map: [batch_size]. Accumulated opacity (alpha) along a ray.
      extras: dict with everything returned by render_rays().
    """
    if c2w is not None:
        # c2w是相机到世界的坐标变换矩阵
        # special case to render full image
        rays_o, rays_d = get_rays(H, W, K, c2w)
    else:
        # use provided ray batch
        # shape: rays[2,4096,3] rays_o[4096,3] rays_d[4096,3]
        rays_o, rays_d = rays

    if use_viewdirs:
        # provide ray directions as input
        viewdirs = rays_d
        if c2w_staticcam is not None:
            # special case to visualize effect of viewdirs
            rays_o, rays_d = get_rays(H, W, K, c2w_staticcam)
        viewdirs = viewdirs / torch.norm(viewdirs, dim=-1, keepdim=True)
        viewdirs = torch.reshape(viewdirs, [-1,3]).float()

    # sh[4096,3]
    sh = rays_d.shape # [..., 3]
    if ndc:
        # for forward facing scenes
        rays_o, rays_d = ndc_rays(H, W, K[0][0], 1., rays_o, rays_d)

    # Create ray batch
    rays_o = torch.reshape(rays_o, [-1,3]).float()
    rays_d = torch.reshape(rays_d, [-1,3]).float()

    # shape: near[4096,1] far[4096,1] 全0或全1
    near, far = near * torch.ones_like(rays_d[...,:1]), far * torch.ones_like(rays_d[...,:1])
    # shape:[4096,3+3+1+1=8]
    rays = torch.cat([rays_o, rays_d, near, far], -1)
    if use_viewdirs:
        rays = torch.cat([rays, viewdirs], -1)

    # Render and reshape
    # chunk默认值是1024*32=32768
    all_ret = batchify_rays(rays, chunk, **kwargs)
    for k in all_ret:
        k_sh = list(sh[:-1]) + list(all_ret[k].shape[1:])
        all_ret[k] = torch.reshape(all_ret[k], k_sh)

    # raw和另外三个分开
    k_extract = ['rgb_map', 'disp_map', 'acc_map']
    ret_list = [all_ret[k] for k in k_extract]
    ret_dict = {k : all_ret[k] for k in all_ret if k not in k_extract}
    return ret_list + [ret_dict]

batchify_rays()

将光束作为一个batch，chunk是并行处理的光束数量，ret是一个chunk（1024×32=32768）的结果，all_ret是一个batch的结果

def batchify_rays(rays_flat, chunk=1024*32, **kwargs):
    """Render rays in smaller minibatches to avoid OOM.
    """
    all_ret = {}
    # shape: rays_flat[4096,8]
    for i in range(0, rays_flat.shape[0], chunk):
        # ret是一个字典,shape:rgb_map[4096,3] disp_map[4096] acc_map[4096] raw[4096,64,4]
        ret = render_rays(rays_flat[i:i+chunk], **kwargs)
        # 每一个key对应一个list，list包含了所有的ret对应key的value
        for k in ret:
            if k not in all_ret:
                all_ret[k] = []
            all_ret[k].append(ret[k])

    all_ret = {k : torch.cat(all_ret[k], 0) for k in all_ret}
    return all_ret

render_rays()

def render_rays(ray_batch,
                network_fn,
                network_query_fn,
                N_samples,
                retraw=False,
                lindisp=False,
                perturb=0.,
                N_importance=0,
                network_fine=None,
                white_bkgd=False,
                raw_noise_std=0.,
                verbose=False,
                pytest=False):
    """Volumetric rendering.
    Args:
      ray_batch: array of shape [batch_size, ...]. All information necessary
        for sampling along a ray, including: ray origin, ray direction, min
        dist, max dist, and unit-magnitude viewing direction.
      network_fn: function. Model for predicting RGB and density at each point
        in space. 用于预测每个点的 RGB 和密度的模型
      network_query_fn: function used for passing queries to network_fn.
      N_samples: int. Number of different times to sample along each ray.每条射线上的采样次数
      retraw: bool. If True, include model's raw, unprocessed predictions.
      lindisp: bool. If True, sample linearly in inverse depth rather than in depth.
      perturb: float, 0 or 1. If non-zero, each ray is sampled at stratified
        random points in time.
      N_importance: int. Number of additional times to sample along each ray.
        These samples are only passed to network_fine.
      network_fine: "fine" network with same spec as network_fn.
      white_bkgd: bool. If True, assume a white background.
      raw_noise_std: ...
      verbose: bool. If True, print more debugging info.
    Returns:
      rgb_map: [num_rays, 3]. Estimated RGB color of a ray. Comes from fine model.
      disp_map: [num_rays]. Disparity map. 1 / depth.
      acc_map: [num_rays]. Accumulated opacity along each ray. Comes from fine model.
      raw: [num_rays, num_samples, 4]. Raw predictions from model.
      rgb0: See rgb_map. Output for coarse model.
      disp0: See disp_map. Output for coarse model.
      acc0: See acc_map. Output for coarse model.
      z_std: [num_rays]. Standard deviation of distances along ray for each
        sample.
    """
    # 从ray_batch提取需要的数据
    # 光束数量默认4096
    N_rays = ray_batch.shape[0]
    rays_o, rays_d = ray_batch[:,0:3], ray_batch[:,3:6] # [N_rays, 3] each
    viewdirs = ray_batch[:,-3:] if ray_batch.shape[-1] > 8 else None
    # shape: bounds[4096,1,2] near[4096,1] far[4096,1]
    bounds = torch.reshape(ray_batch[...,6:8], [-1,1,2])
    near, far = bounds[...,0], bounds[...,1] # [-1,1]

    # 每个光束上取N_samples个点,默认64个
    t_vals = torch.linspace(0., 1., steps=N_samples)
    if not lindisp:
        z_vals = near * (1.-t_vals) + far * (t_vals)
    else:
        z_vals = 1./(1./near * (1.-t_vals) + 1./far * (t_vals))

    z_vals = z_vals.expand([N_rays, N_samples])

    if perturb > 0.:
        # get intervals between samples
        mids = .5 * (z_vals[...,1:] + z_vals[...,:-1])
        upper = torch.cat([mids, z_vals[...,-1:]], -1)
        lower = torch.cat([z_vals[...,:1], mids], -1)
        # stratified samples in those intervals
        t_rand = torch.rand(z_vals.shape)

        # Pytest, overwrite u with numpy's fixed random numbers
        if pytest:
            np.random.seed(0)
            t_rand = np.random.rand(*list(z_vals.shape))
            t_rand = torch.Tensor(t_rand)

        z_vals = lower + (upper - lower) * t_rand

    # 光束打到的位置(采样点)，可用来输入网络查询颜色和密度 shape: pts[4096,64,3]
    pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None] # [N_rays, N_samples, 3]


    # raw = run_network(pts)
    # 根据pts,viewdirs进行前向计算。raw[4096,64,4]，最后一个维是RGB+density。
    raw = network_query_fn(pts, viewdirs, network_fn)
    # 这一步相当于是在做volume render，将光束颜色合成图像上的点
    rgb_map, disp_map, acc_map, weights, depth_map = raw2outputs(raw, z_vals, rays_d, raw_noise_std, white_bkgd, pytest=pytest)

    # 下面是有精细网络的情况，会再算一遍上述步骤，然后也封装到ret
    if N_importance > 0:

        # 保存前面的值
        rgb_map_0, disp_map_0, acc_map_0 = rgb_map, disp_map, acc_map

        # 重新采样光束上的点
        z_vals_mid = .5 * (z_vals[...,1:] + z_vals[...,:-1])
        z_samples = sample_pdf(z_vals_mid, weights[...,1:-1], N_importance, det=(perturb==0.), pytest=pytest)
        z_samples = z_samples.detach()

        z_vals, _ = torch.sort(torch.cat([z_vals, z_samples], -1), -1)
        pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None] # [N_rays, N_samples + N_importance, 3]

        run_fn = network_fn if network_fine is None else network_fine
        # raw = run_network(pts, fn=run_fn)
        raw = network_query_fn(pts, viewdirs, run_fn)

        rgb_map, disp_map, acc_map, weights, depth_map = raw2outputs(raw, z_vals, rays_d, raw_noise_std, white_bkgd, pytest=pytest)

    # 不管有无精细网络都要
    # shape: rgb_map[4096,3] disp_map[4096] acc_map[4096]
    ret = {'rgb_map' : rgb_map, 'disp_map' : disp_map, 'acc_map' : acc_map}
    if retraw:
        ret['raw'] = raw
    if N_importance > 0:
        ret['rgb0'] = rgb_map_0
        ret['disp0'] = disp_map_0
        ret['acc0'] = acc_map_0
        ret['z_std'] = torch.std(z_samples, dim=-1, unbiased=False)  # [N_rays]

    for k in ret:
        if (torch.isnan(ret[k]).any() or torch.isinf(ret[k]).any()) and DEBUG:
            print(f"! [Numerical Error] {k} contains nan or inf.")

    return ret

raw2outputs()

把模型的预测转化为有实际意义的表达，输入预测、时间和光束方向，输出光束颜色、视差、密度、每个采样点的权重和深度

def raw2outputs(raw, z_vals, rays_d, raw_noise_std=0, white_bkgd=False, pytest=False):
    """Transforms model's predictions to semantically meaningful values.
    Args:
        raw: [num_rays, num_samples along ray, 4]. Prediction from model.
        z_vals: [num_rays, num_samples along ray]. Integration time.
        rays_d: [num_rays, 3]. Direction of each ray.
    Returns:
        rgb_map: [num_rays, 3]. Estimated RGB color of a ray.
        disp_map: [num_rays]. Disparity map. Inverse of depth map.
        acc_map: [num_rays]. Sum of weights along each ray.
        weights: [num_rays, num_samples]. Weights assigned to each sampled color.
        depth_map: [num_rays]. Estimated distance to object.
    """
    raw2alpha = lambda raw, dists, act_fn=F.relu: 1.-torch.exp(-act_fn(raw)*dists)

    dists = z_vals[...,1:] - z_vals[...,:-1]
    dists = torch.cat([dists, torch.Tensor([1e10]).expand(dists[...,:1].shape)], -1)  # [N_rays, N_samples]

    dists = dists * torch.norm(rays_d[...,None,:], dim=-1)

    # 获取模型预测的每个点的颜色
    rgb = torch.sigmoid(raw[...,:3])  # [N_rays, N_samples, 3]
    noise = 0.
    if raw_noise_std > 0.:
        noise = torch.randn(raw[...,3].shape) * raw_noise_std

        # Overwrite randomly sampled data if pytest
        if pytest:
            np.random.seed(0)
            noise = np.random.rand(*list(raw[...,3].shape)) * raw_noise_std
            noise = torch.Tensor(noise)

    # 给密度加噪音
    alpha = raw2alpha(raw[...,3] + noise, dists)  # [N_rays, N_samples]
    # weights = alpha * tf.math.cumprod(1.-alpha + 1e-10, -1, exclusive=True)
    weights = alpha * torch.cumprod(torch.cat([torch.ones((alpha.shape[0], 1)), 1.-alpha + 1e-10], -1), -1)[:, :-1]
    rgb_map = torch.sum(weights[...,None] * rgb, -2)  # [N_rays, 3]

    depth_map = torch.sum(weights * z_vals, -1)
    disp_map = 1./torch.max(1e-10 * torch.ones_like(depth_map), depth_map / torch.sum(weights, -1))
    acc_map = torch.sum(weights, -1)

    if white_bkgd:
        rgb_map = rgb_map + (1.-acc_map[...,None])

    return rgb_map, disp_map, acc_map, weights, depth_map

render_path()

根据pose等信息获得颜色和视差

def render_path(render_poses, hwf, K, chunk, render_kwargs, gt_imgs=None, savedir=None, render_factor=0):

    H, W, focal = hwf

    if render_factor!=0:
        # Render downsampled for speed
        H = H//render_factor
        W = W//render_factor
        focal = focal/render_factor

    rgbs = []
    disps = []

    t = time.time()
    for i, c2w in enumerate(tqdm(render_poses)):
        print(i, time.time() - t)
        t = time.time()
        rgb, disp, acc, _ = render(H, W, K, chunk=chunk, c2w=c2w[:3,:4], **render_kwargs)
        rgbs.append(rgb.cpu().numpy())
        disps.append(disp.cpu().numpy())
        if i==0:
            print(rgb.shape, disp.shape)

        """
        if gt_imgs is not None and render_factor==0:
            p = -10. * np.log10(np.mean(np.square(rgb.cpu().numpy() - gt_imgs[i])))
            print(p)
        """

        if savedir is not None:
            rgb8 = to8b(rgbs[-1])
            filename = os.path.join(savedir, '{:03d}.png'.format(i))
            imageio.imwrite(filename, rgb8)


    rgbs = np.stack(rgbs, 0)
    disps = np.stack(disps, 0)

    return rgbs, disps

run_nerf_helpers.py

这个里面写了一些必要的函数

class NeRF()

这个类用于创建model，alpha输出的是密度，rgb是颜色，一个batch是1024个光束，也就是一个光束采样64个点

class NeRF(nn.Module):
    def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False):
        """ 
        """
        super(NeRF, self).__init__()
        self.D = D
        self.W = W
        # 输入的通道
        self.input_ch = input_ch
        # 输入的视角
        self.input_ch_views = input_ch_views
        self.skips = skips
        self.use_viewdirs = use_viewdirs
        
        self.pts_linears = nn.ModuleList(
            [nn.Linear(input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + input_ch, W) for i in range(D-1)])
        
        ### Implementation according to the official code release (https://github.com/bmild/nerf/blob/master/run_nerf_helpers.py#L104-L105)
        self.views_linears = nn.ModuleList([nn.Linear(input_ch_views + W, W//2)])

        ### Implementation according to the paper
        # self.views_linears = nn.ModuleList(
        #     [nn.Linear(input_ch_views + W, W//2)] + [nn.Linear(W//2, W//2) for i in range(D//2)])
        
        if use_viewdirs:
            self.feature_linear = nn.Linear(W, W)
            self.alpha_linear = nn.Linear(W, 1)
            self.rgb_linear = nn.Linear(W//2, 3)
        else:
            self.output_linear = nn.Linear(W, output_ch)

    def forward(self, x):
        input_pts, input_views = torch.split(x, [self.input_ch, self.input_ch_views], dim=-1)
        h = input_pts
        for i, l in enumerate(self.pts_linears):
            h = self.pts_linears[i](h)
            h = F.relu(h)
            if i in self.skips:
                h = torch.cat([input_pts, h], -1)

        if self.use_viewdirs:
            alpha = self.alpha_linear(h)
            feature = self.feature_linear(h)
            h = torch.cat([feature, input_views], -1)
        
            for i, l in enumerate(self.views_linears):
                h = self.views_linears[i](h)
                h = F.relu(h)

            rgb = self.rgb_linear(h)
            outputs = torch.cat([rgb, alpha], -1)
        else:
            outputs = self.output_linear(h)

        return outputs    

    def load_weights_from_keras(self, weights):
        assert self.use_viewdirs, "Not implemented if use_viewdirs=False"
        
        # Load pts_linears
        for i in range(self.D):
            idx_pts_linears = 2 * i
            self.pts_linears[i].weight.data = torch.from_numpy(np.transpose(weights[idx_pts_linears]))    
            self.pts_linears[i].bias.data = torch.from_numpy(np.transpose(weights[idx_pts_linears+1]))
        
        # Load feature_linear
        idx_feature_linear = 2 * self.D
        self.feature_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_feature_linear]))
        self.feature_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_feature_linear+1]))

        # Load views_linears
        idx_views_linears = 2 * self.D + 2
        self.views_linears[0].weight.data = torch.from_numpy(np.transpose(weights[idx_views_linears]))
        self.views_linears[0].bias.data = torch.from_numpy(np.transpose(weights[idx_views_linears+1]))

        # Load rgb_linear
        idx_rbg_linear = 2 * self.D + 4
        self.rgb_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_rbg_linear]))
        self.rgb_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_rbg_linear+1]))

        # Load alpha_linear
        idx_alpha_linear = 2 * self.D + 6
        self.alpha_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_alpha_linear]))
        self.alpha_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_alpha_linear+1]))

get_rays_np()

获得光束的方法

def get_rays_np(H, W, K, c2w):
    # 生成网格点坐标矩阵，i和j分别表示每个像素的坐标
    i, j = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy')
    dirs = np.stack([(i-K[0][2])/K[0][0], -(j-K[1][2])/K[1][1], -np.ones_like(i)], -1)
    # Rotate ray directions from camera frame to the world frame
    # 将光线方向从相机旋转到世界
    rays_d = np.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)  # dot product, equals to: [c2w.dot(dir) for dir in dirs]
    # Translate camera frame's origin to the world frame. It is the origin of all rays.
    # 将相机框架的原点转换为世界框架，它是所有光线的起源
    rays_o = np.broadcast_to(c2w[:3,-1], np.shape(rays_d))
    return rays_o, rays_d

ndc_rays()

把光线的原点移动到near平面

def ndc_rays(H, W, focal, near, rays_o, rays_d):
    # Shift ray origins to near plane
    t = -(near + rays_o[...,2]) / rays_d[...,2]
    rays_o = rays_o + t[...,None] * rays_d
    
    # Projection
    o0 = -1./(W/(2.*focal)) * rays_o[...,0] / rays_o[...,2]
    o1 = -1./(H/(2.*focal)) * rays_o[...,1] / rays_o[...,2]
    o2 = 1. + 2. * near / rays_o[...,2]

    d0 = -1./(W/(2.*focal)) * (rays_d[...,0]/rays_d[...,2] - rays_o[...,0]/rays_o[...,2])
    d1 = -1./(H/(2.*focal)) * (rays_d[...,1]/rays_d[...,2] - rays_o[...,1]/rays_o[...,2])
    d2 = -2. * near / rays_o[...,2]
    
    rays_o = torch.stack([o0,o1,o2], -1)
    rays_d = torch.stack([d0,d1,d2], -1)
    
    return rays_o, rays_d

接下来我们了解一下数据是怎么读取的

load_llff.py

_load_data()

def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):
    # 读取npy文件 
    poses_arr = np.load(os.path.join(basedir, 'poses_bounds.npy'))
    poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1,2,0])
    bds = poses_arr[:, -2:].transpose([1,0])
    
    # 单张图片
    img0 = [os.path.join(basedir, 'images', f) for f in sorted(os.listdir(os.path.join(basedir, 'images'))) \
            if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')][0]
    # 获取单张图片的shape
    sh = imageio.imread(img0).shape
    
    sfx = ''
    
    if factor is not None:
        sfx = '_{}'.format(factor)
        _minify(basedir, factors=[factor])
        factor = factor
    elif height is not None:
        factor = sh[0] / float(height)
        width = int(sh[1] / factor)
        _minify(basedir, resolutions=[[height, width]])
        sfx = '_{}x{}'.format(width, height)
    elif width is not None:
        factor = sh[1] / float(width)
        height = int(sh[0] / factor)
        _minify(basedir, resolutions=[[height, width]])
        sfx = '_{}x{}'.format(width, height)
    else:
        factor = 1
    
    imgdir = os.path.join(basedir, 'images' + sfx)
    if not os.path.exists(imgdir):
        print( imgdir, 'does not exist, returning' )
        return
    
    # 包含了目标数据的路径
    imgfiles = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir)) if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')]
    if poses.shape[-1] != len(imgfiles):
        print( 'Mismatch between imgs {} and poses {} !!!!'.format(len(imgfiles), poses.shape[-1]) )
        return
    
    sh = imageio.imread(imgfiles[0]).shape
    poses[:2, 4, :] = np.array(sh[:2]).reshape([2, 1])
    poses[2, 4, :] = poses[2, 4, :] * 1./factor
    
    if not load_imgs:
        return poses, bds
    
    def imread(f):
        if f.endswith('png'):
            return imageio.imread(f, ignoregamma=True)
        else:
            return imageio.imread(f)
        
    # 读取所有图像数据并把值缩小到0-1之间
    imgs = imgs = [imread(f)[...,:3]/255. for f in imgfiles]
    # 
    imgs = np.stack(imgs, -1)  
    
    print('Loaded image data', imgs.shape, poses[:,-1,0])
    return poses, bds, imgs

_minify()

这个函数主要负责创建目标分辨率的数据集

def _minify(basedir, factors=[], resolutions=[]):
    # 判断是否需要加载，如果不存在对应下采样或者分辨率的文件夹就需要加载
    needtoload = False
    for r in factors:
        imgdir = os.path.join(basedir, 'images_{}'.format(r))
        if not os.path.exists(imgdir):
            needtoload = True
    for r in resolutions:
        imgdir = os.path.join(basedir, 'images_{}x{}'.format(r[1], r[0]))
        if not os.path.exists(imgdir):
            needtoload = True
    if not needtoload:
        return
    
    from shutil import copy
    from subprocess import check_output
    
    imgdir = os.path.join(basedir, 'images')
    imgs = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir))]
    imgs = [f for f in imgs if any([f.endswith(ex) for ex in ['JPG', 'jpg', 'png', 'jpeg', 'PNG']])]
    imgdir_orig = imgdir
    
    wd = os.getcwd()

    for r in factors + resolutions:
        if isinstance(r, int):
            name = 'images_{}'.format(r)
            resizearg = '{}%'.format(100./r)
        else:
            name = 'images_{}x{}'.format(r[1], r[0])
            resizearg = '{}x{}'.format(r[1], r[0])
        imgdir = os.path.join(basedir, name)
        if os.path.exists(imgdir):
            continue
            
        print('Minifying', r, basedir)
        
        os.makedirs(imgdir)
        check_output('cp {}/* {}'.format(imgdir_orig, imgdir), shell=True)
        
        ext = imgs[0].split('.')[-1]
        args = ' '.join(['mogrify', '-resize', resizearg, '-format', 'png', '*.{}'.format(ext)])
        print(args)
        os.chdir(imgdir) # 修改当前工作目录
        check_output(args, shell=True)
        os.chdir(wd)
        
        if ext != 'png':
            check_output('rm {}/*.{}'.format(imgdir, ext), shell=True)
            print('Removed duplicates')
        print('Done')
            

load_llff_data()

def load_llff_data(basedir, factor=8, recenter=True, bd_factor=.75, spherify=False, path_zflat=False):

    poses, bds, imgs = _load_data(basedir, factor=factor) # factor=8 downsamples original imgs by 8x
    print('Loaded', basedir, bds.min(), bds.max())
    
    # Correct rotation matrix ordering and move variable dim to axis 0
    poses = np.concatenate([poses[:, 1:2, :], -poses[:, 0:1, :], poses[:, 2:, :]], 1)
    poses = np.moveaxis(poses, -1, 0).astype(np.float32)
    imgs = np.moveaxis(imgs, -1, 0).astype(np.float32)
    images = imgs
    bds = np.moveaxis(bds, -1, 0).astype(np.float32)
    
    # Rescale if bd_factor is provided
    # sc是进行边界缩放的比例
    sc = 1. if bd_factor is None else 1./(bds.min() * bd_factor)
    # pose也就要对应缩放
    poses[:,:3,3] *= sc
    bds *= sc
    
    if recenter:
        # 修改pose（shape=图像数,通道数,5）前四列的值，只有最后一列（高、宽、焦距）不变  
        poses = recenter_poses(poses)
        
    if spherify:
        poses, render_poses, bds = spherify_poses(poses, bds)

    else:
        
        # shape=(3,5)相当于汇集了所有图像
        c2w = poses_avg(poses) 
        print('recentered', c2w.shape)
        print(c2w[:3,:4])

        ## Get spiral
        # Get average pose
        # 3*1
        up = normalize(poses[:, :3, 1].sum(0))

        # Find a reasonable "focus depth" for this dataset
        close_depth, inf_depth = bds.min()*.9, bds.max()*5.
        dt = .75
        mean_dz = 1./(((1.-dt)/close_depth + dt/inf_depth))
        # 焦距
        focal = mean_dz

        # Get radii for spiral path
        shrink_factor = .8
        zdelta = close_depth * .2
        # 获取所有poses的3列，shape(图片数,3)
        tt = poses[:,:3,3] # ptstocam(poses[:3,3,:].T, c2w).T
        # 求90百分位的值
        rads = np.percentile(np.abs(tt), 90, 0)
        c2w_path = c2w
        N_views = 120
        N_rots = 2
        if path_zflat:
            # zloc = np.percentile(tt, 10, 0)[2]
            zloc = -close_depth * .1
            c2w_path[:3,3] = c2w_path[:3,3] + zloc * c2w_path[:3,2]
            rads[2] = 0.
            N_rots = 1
            N_views/=2

        # Generate poses for spiral path
        # 一个list，有120（由N_views决定）个元素，每个元素shape(3,5)
        render_poses = render_path_spiral(c2w_path, up, rads, focal, zdelta, zrate=.5, rots=N_rots, N=N_views)
        
            
    render_poses = np.array(render_poses).astype(np.float32)

    c2w = poses_avg(poses)
    print('Data:')
    print(poses.shape, images.shape, bds.shape)
    
    # shape 图片数
    dists = np.sum(np.square(c2w[:3,3] - poses[:,:3,3]), -1)
    # 取到值最小的索引
    i_test = np.argmin(dists)
    print('HOLDOUT view is', i_test)
    
    images = images.astype(np.float32)
    poses = poses.astype(np.float32)

    # images (图片数,高,宽,3通道), poses (图片数,3通道,5) ,bds (图片数,2) render_poses(N_views,图片数,5)，i_test为一个索引数字
    return images, poses, bds, render_poses, i_test

render_path_spiral()

def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
    render_poses = []
    rads = np.array(list(rads) + [1.])
    hwf = c2w[:,4:5]
    
    for theta in np.linspace(0., 2. * np.pi * rots, N+1)[:-1]:
        c = np.dot(c2w[:3,:4], np.array([np.cos(theta), -np.sin(theta), -np.sin(theta*zrate), 1.]) * rads) 
        z = normalize(c - np.dot(c2w[:3,:4], np.array([0,0,-focal, 1.])))
        render_poses.append(np.concatenate([viewmatrix(z, up, c), hwf], 1))
    return render_poses