教程 1: 如何编写配置文件

XRNeRF 使用 python 文件作为配置文件。其配置文件系统的设计将模块化与继承整合进来，方便用户进行各种实验。 XRNeRF 提供的所有配置文件都放置在 $PROJECT/configs 文件夹下。

• 教程 1: 如何编写配置文件
  • 配置文件组成部分

配置文件组成部分

配置文件的内容在逻辑上可以分为3个部分:

• 训练
• 模型
• 数据

下面的内容将会逐部分介绍配置文件

• 训练 训练配置部分包含了控制训练过程的各类参数，包括optimizer, hooks, runner等等

  import os
  from datetime import datetime
  
  method = 'nerf' # nerf方法
  
  # optimizer 参数
  optimizer = dict(type='Adam', lr=5e-4, betas=(0.9, 0.999))
  optimizer_config = dict(grad_clip=None)
  
  max_iters = 20000 # 训练多少个iter
  lr_config = dict(policy='step', step=500 * 1000, gamma=0.1, by_epoch=False) # 学习率和衰减
  checkpoint_config = dict(interval=5000, by_epoch=False) # 保存checkpoint的间隔
  log_level = 'INFO'
  log_config = dict(interval=5000,
                  by_epoch=False,
                  hooks=[dict(type='TextLoggerHook')])
  workflow = [('train', 5000), ('val', 1)] # 循环: 每训练 5000 iters, 验证 1 iter
  
  # hooks
  # 'params' 是数值型参数, 'variables' 是代码运行上下面出现的变量
  train_hooks = [
      dict(type='SetValPipelineHook',
          params=dict(),
          variables=dict(valset='valset')),
      dict(type='ValidateHook',
          params=dict(save_folder='visualizations/validation')),
      dict(type='SaveSpiralHook',
          params=dict(save_folder='visualizations/spiral')),
      dict(type='PassIterHook', params=dict()),  # 将当前iter数告诉dataset
  ]
  test_hooks = [
      dict(type='SetValPipelineHook',
          params=dict(),
          variables=dict(valset='testset')),
      dict(type='TestHook', params=dict()),
  ]
  
  # runner
  train_runner = dict(type='NerfTrainRunner')
  test_runner = dict(type='NerfTestRunner')
  
  # runtime settings
  num_gpus = 1
  distributed = (num_gpus > 1)  # 是否使用 ddp
  work_dir = './work_dirs/nerfsv3/nerf_#DATANAME#_base01/' # 保存运行时产生文件的位置
  timestamp = datetime.now().strftime('%d-%b-%H-%M') # 保证每次的workspace都不同
  
  # some shared params by model and data, to avoid define twice
  dataset_type = 'blender'
  no_batching = True  # 每次选择1张图片来抽取射线
  no_ndc = True
  
  white_bkgd = True  # 渲染时背景设定为全白
  is_perturb = True  # set to 0. for no jitter, 1. for jitter
  use_viewdirs = True  # use full 5D input instead of 3D
  N_rand_per_sampler = 1024 * 4  # 在取多少根射线 在 get_item() 函数中使用
  lindisp = False  # sampling linearly in disparity rather than depth
  N_samples = 64  # 在coarse模型中输入多少根射线
  
  # resume_from = os.path.join(work_dir, 'latest.pth')
  # load_from = os.path.join(work_dir, 'latest.pth')
  

• 模型 模型部分的配置信息，定义了网络模型结构，一个network通常由embedder, mlp 和 render组成。

  model = dict(
      type='NerfNetwork', # network 类名字
      cfg=dict(
          phase='train',  # 'train' or 'test'
          N_importance=128,  # number of additional fine samples per ray
          is_perturb=is_perturb, # see above
          chunk=1024 * 32,  # mainly work for val, to avoid oom
          bs_data='rays_o',  # the data's shape indicates the real batch-size, this's also the num of rays
      ),
      mlp=dict(  # coarse mlp model
          type='NerfMLP', # mlp class name
          skips=[4],
          netdepth=8,  # layers in network
          netwidth=256,  # channels per layer
          netchunk=1024 * 32,  # to avoid oom
          output_ch=5,  # 5 if cfg.N_importance>0 else 4
          use_viewdirs=use_viewdirs,
          embedder=dict(
              type='BaseEmbedder', # embedder class name
              i_embed=0,  # set 0 for default positional encoding, -1 for none
              multires=10,  # log2 of max freq for positional encoding (3D location)
              multires_dirs=4,  # this is 'multires_views' in origin codes, log2 of max freq for positional encoding (2D direction)
          ),
      ),
      mlp_fine=dict(  # fine model
          type='NerfMLP',
          skips=[4],
          netdepth=8,
          netwidth=256,
          netchunk=1024 * 32,
          output_ch=5,
          use_viewdirs=use_viewdirs,
          embedder=dict(
              type='BaseEmbedder',
              i_embed=0,
              multires=10,
              multires_dirs=4,
          ),
      ),
      render=dict(
          type='NerfRender', # render cloass name
          white_bkgd=white_bkgd,  # see above
          raw_noise_std=0,  # std dev of noise added to regularize sigma_a output, 1e0 recommended
      ),
  )

• 数据 数据部分的配置信息，定义了数据集类型，数据的处理流程，batchsize等等信息。

  basedata_cfg = dict(
      dataset_type=dataset_type,
      datadir='data/nerf_synthetic/#DATANAME#',
      half_res=True,  # load blender synthetic data at 400x400 instead of 800x800
      testskip=
      8,  # will load 1/N images from test/val sets, useful for large datasets like deepvoxels
      white_bkgd=white_bkgd,
      is_batching=False,  # True for blender, False for llff
      mode='train',
  )
  
  traindata_cfg = basedata_cfg.copy()
  valdata_cfg = basedata_cfg.copy()
  testdata_cfg = basedata_cfg.copy()
  
  traindata_cfg.update(dict())
  valdata_cfg.update(dict(mode='val'))
  testdata_cfg.update(dict(mode='test', testskip=0))
  
  train_pipeline = [
      dict(type='Sample'),
      dict(type='DeleteUseless', keys=['images', 'poses', 'i_data', 'idx']),
      dict(type='ToTensor', keys=['pose', 'target_s']),
      dict(type='GetRays'),
      dict(type='SelectRays',
          sel_n=N_rand_per_sampler,
          precrop_iters=500,
          precrop_frac=0.5),  # in the first 500 iter, select rays inside center of image
      dict(type='GetViewdirs', enable=use_viewdirs),
      dict(type='ToNDC', enable=(not no_ndc)),
      dict(type='GetBounds'),
      dict(type='GetZvals', lindisp=lindisp,
          N_samples=N_samples),  # N_samples: number of coarse samples per ray
      dict(type='PerturbZvals', enable=is_perturb),
      dict(type='GetPts'),
      dict(type='DeleteUseless', keys=['pose', 'iter_n']),
  ]
  
  test_pipeline = [
      dict(type='ToTensor', keys=['pose']),
      dict(type='GetRays'),
      dict(type='FlattenRays'),
      dict(type='GetViewdirs', enable=use_viewdirs),
      dict(type='ToNDC', enable=(not no_ndc)),
      dict(type='GetBounds'),
      dict(type='GetZvals', lindisp=lindisp, N_samples=N_samples),
      dict(type='PerturbZvals', enable=False),  # do not perturb when test
      dict(type='GetPts'),
      dict(type='DeleteUseless', keys=['pose']),
  ]
  data = dict(
      train_loader=dict(batch_size=1, num_workers=4),
      train=dict(
          type='SceneBaseDataset',
          cfg=traindata_cfg,
          pipeline=train_pipeline,
      ),
      val_loader=dict(batch_size=1, num_workers=0),
      val=dict(
          type='SceneBaseDataset',
          cfg=valdata_cfg,
          pipeline=test_pipeline,
      ),
      test_loader=dict(batch_size=1, num_workers=0),
      test=dict(
          type='SceneBaseDataset',
          cfg=testdata_cfg,
          pipeline=test_pipeline,  # same pipeline as validation
      ),
  )