├── .gitignore ├── 300000.pth ├── LICENSE ├── README.md ├── data └── nerf_synthetic │ └── lego │ ├── train │ ├── r_0.png │ ├── r_1.png │ ├── r_10.png │ ├── r_11.png │ ├── r_12.png │ ├── r_13.png │ ├── r_14.png │ ├── r_15.png │ ├── r_16.png │ ├── r_17.png │ ├── r_18.png │ ├── r_19.png │ ├── r_2.png │ ├── r_20.png │ ├── r_21.png │ ├── r_22.png │ ├── r_23.png │ ├── r_24.png │ ├── r_25.png │ ├── r_26.png │ ├── r_27.png │ ├── r_28.png │ ├── r_29.png │ ├── r_3.png │ ├── r_30.png │ ├── r_31.png │ ├── r_32.png │ ├── r_33.png │ ├── r_34.png │ ├── r_35.png │ ├── r_36.png │ ├── r_37.png │ ├── r_38.png │ ├── r_39.png │ ├── r_4.png │ ├── r_40.png │ ├── r_41.png │ ├── r_42.png │ ├── r_43.png │ ├── r_44.png │ ├── r_45.png │ ├── r_46.png │ ├── r_47.png │ ├── r_48.png │ ├── r_49.png │ ├── r_5.png │ ├── r_50.png │ ├── r_51.png │ ├── r_52.png │ ├── r_53.png │ ├── r_54.png │ ├── r_55.png │ ├── r_56.png │ ├── r_57.png │ ├── r_58.png │ ├── r_59.png │ ├── r_6.png │ ├── r_60.png │ ├── r_61.png │ ├── r_62.png │ ├── r_63.png │ ├── r_64.png │ ├── r_65.png │ ├── r_66.png │ ├── r_67.png │ ├── r_68.png │ ├── r_69.png │ ├── r_7.png │ ├── r_70.png │ ├── r_71.png │ ├── r_72.png │ ├── r_73.png │ ├── r_74.png │ ├── r_75.png │ ├── r_76.png │ ├── r_77.png │ ├── r_78.png │ ├── r_79.png │ ├── r_8.png │ ├── r_80.png │ ├── r_81.png │ ├── r_82.png │ ├── r_83.png │ ├── r_84.png │ ├── r_85.png │ ├── r_86.png │ ├── r_87.png │ ├── r_88.png │ ├── r_89.png │ ├── r_9.png │ ├── r_90.png │ ├── r_91.png │ ├── r_92.png │ ├── r_93.png │ ├── r_94.png │ ├── r_95.png │ ├── r_96.png │ ├── r_97.png │ ├── r_98.png │ └── r_99.png │ └── transforms_train.json ├── imgs-full-demo ├── 1000.png ├── 10000.png ├── 100000.png ├── 1500.png ├── 15000.png ├── 20000.png ├── 200000.png ├── 300000.png ├── 500.png ├── 5000.png └── 50000.png ├── rotate360 ├── 000.png ├── 001.png ├── 007.png ├── 008.png ├── 015.png ├── 022.png └── 023.png └── train-nerf.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | /videos 131 | /imgs 132 | /ckpt -------------------------------------------------------------------------------- /300000.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shouxieai/nerf/c56f8179fca38c84200e230e7cb1133f765ec998/300000.pth -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 手写AI 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Get Start 2 | 1. Run train `python train-nerf.py --half-resolution` 3 | 4 | # Run Demo 5 | 1. Run `python train-nerf.py --make-video360` 6 | - Produce a video with 360 degree rendering 7 | 8 | # Demo 9 | ![](rotate360/008.png) 10 | 11 | # Reference 12 | 1. https://github.com/bmild/nerf 13 | 2. https://github.com/yenchenlin/nerf-pytorch 14 | -------------------------------------------------------------------------------- /data/nerf_synthetic/lego/train/r_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shouxieai/nerf/c56f8179fca38c84200e230e7cb1133f765ec998/data/nerf_synthetic/lego/train/r_0.png -------------------------------------------------------------------------------- /data/nerf_synthetic/lego/train/r_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shouxieai/nerf/c56f8179fca38c84200e230e7cb1133f765ec998/data/nerf_synthetic/lego/train/r_1.png -------------------------------------------------------------------------------- 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编码为cos、sin不同频率的叠加，使得连续值可以具有足够的区分性 16 | # 2. 视图独立性，View Dependent 17 | # - 输入不仅仅是光线采样点的x、y、z坐标，加上了视图依赖，即x、y、z、theta、pi，5d输入，此时多了射线所在视图 18 | # 3. 分层采样，Hierarchical sampling 19 | # - 将渲染分为两级，由于第一级别的模型是均匀采样，而实际会有很多无效的采样（即对颜色没有贡献的区域会占比太多），在模型 20 | # 中看来，就是某些点的梯度为0，对模型训练没有贡献 21 | # - 因此采用两级模型，model、fine，model模型使用均匀采样，推断后得到weights的分布，通过对weights分布进行重采样，使得采样点 22 | # 更加集中在更重要的区域，今儿使得参与训练的点大都是有效的点。所以model作为一级推理，fine则推理重采样后的点 23 | # 24 | # x. 拓展，对于射线的方向和原点的理解，需要具有基本的3d变换知识，建议看GAMES101的前5章补充知识 25 | # PSNR是峰值信噪比，表示重建的逼真程度 26 | # 这三个环节有了，效果就会非常逼真，但是某些细节上还是存在不足。另外训练时间非常关键 27 | 28 | class BlenderProvider: 29 | def __init__(self, root, transforms_file, half_resolution=True): 30 | 31 | self.meta = json.load(open(os.path.join(root, transforms_file), "r")) 32 | self.root = root 33 | self.frames = self.meta["frames"] 34 | self.images = [] 35 | self.poses = [] 36 | self.camera_angle_x = self.meta["camera_angle_x"] 37 | 38 | for frame in self.frames: 39 | image_file = os.path.join(self.root, frame["file_path"] + ".png") 40 | image = imageio.imread(image_file) 41 | 42 | if half_resolution: 43 | image = cv2.resize(image, dsize=None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA) 44 | 45 | self.images.append(image) 46 | self.poses.append(frame["transform_matrix"]) 47 | 48 | self.poses = np.stack(self.poses) 49 | self.images = (np.stack(self.images) / 255.0).astype(np.float32) 50 | self.width = self.images.shape[2] 51 | self.height = self.images.shape[1] 52 | self.focal = 0.5 * self.width / np.tan(0.5 * self.camera_angle_x) 53 | 54 | alpha = self.images[..., [3]] 55 | rgb = self.images[..., :3] 56 | self.images = rgb * alpha + (1 - alpha) 57 | 58 | 59 | class NeRFDataset: 60 | def __init__(self, provider, batch_size=1024, device="cuda"): 61 | 62 | self.images = provider.images 63 | self.poses = provider.poses 64 | self.focal = provider.focal 65 | self.width = provider.width 66 | self.height = provider.height 67 | self.batch_size = batch_size 68 | self.num_image = len(self.images) 69 | self.precrop_iters = 500 70 | self.precrop_frac = 0.5 71 | self.niter = 0 72 | self.device = device 73 | self.initialize() 74 | 75 | 76 | def initialize(self): 77 | 78 | warange = torch.arange(self.width, dtype=torch.float32, device=self.device) 79 | harange = torch.arange(self.height, dtype=torch.float32, device=self.device) 80 | y, x = torch.meshgrid(harange, warange) 81 | 82 | self.transformed_x = (x - self.width * 0.5) / self.focal 83 | self.transformed_y = (y - self.height * 0.5) / self.focal 84 | 85 | # pre center crop 86 | self.precrop_index = torch.arange(self.width * self.height).view(self.height, self.width) 87 | 88 | dH = int(self.height // 2 * self.precrop_frac) 89 | dW = int(self.width // 2 * self.precrop_frac) 90 | self.precrop_index = self.precrop_index[ 91 | self.height // 2 - dH:self.height // 2 + dH, 92 | self.width // 2 - dW:self.width // 2 + dW 93 | ].reshape(-1) 94 | 95 | poses = torch.cuda.FloatTensor(self.poses, device=self.device) 96 | all_ray_dirs, all_ray_origins = [], [] 97 | 98 | for i in range(len(self.images)): 99 | ray_dirs, ray_origins = self.make_rays(self.transformed_x, self.transformed_y, poses[i]) 100 | all_ray_dirs.append(ray_dirs) 101 | all_ray_origins.append(ray_origins) 102 | 103 | self.all_ray_dirs = torch.stack(all_ray_dirs, dim=0) 104 | self.all_ray_origins = torch.stack(all_ray_origins, dim=0) 105 | self.images = torch.cuda.FloatTensor(self.images, device=self.device).view(self.num_image, -1, 3) 106 | 107 | 108 | def __getitem__(self, index): 109 | self.niter += 1 110 | 111 | ray_dirs = self.all_ray_dirs[index] 112 | ray_oris = self.all_ray_origins[index] 113 | img_pixels = self.images[index] 114 | if self.niter < self.precrop_iters: 115 | ray_dirs = ray_dirs[self.precrop_index] 116 | ray_oris = ray_oris[self.precrop_index] 117 | img_pixels = img_pixels[self.precrop_index] 118 | 119 | nrays = self.batch_size 120 | select_inds = np.random.choice(ray_dirs.shape[0], size=[nrays], replace=False) 121 | ray_dirs = ray_dirs[select_inds] 122 | ray_oris = ray_oris[select_inds] 123 | img_pixels = img_pixels[select_inds] 124 | 125 | # dirs是指：direction 126 | # ori是指： origin 127 | return ray_dirs, ray_oris, img_pixels 128 | 129 | 130 | def __len__(self): 131 | return self.num_image 132 | 133 | 134 | def make_rays(self, x, y, pose): 135 | 136 | # 100, 100, 3 137 | # 坐标系在-y，-z方向上 138 | directions = torch.stack([x, -y, -torch.ones_like(x)], dim=-1) 139 | camera_matrix = pose[:3, :3] 140 | 141 | # 10000 x 3 142 | ray_dirs = directions.reshape(-1, 3) @ camera_matrix.T 143 | ray_origin = pose[:3, 3].view(1, 3).repeat(len(ray_dirs), 1) 144 | return ray_dirs, ray_origin 145 | 146 | 147 | def get_test_item(self, index=0): 148 | 149 | ray_dirs = self.all_ray_dirs[index] 150 | ray_oris = self.all_ray_origins[index] 151 | img_pixels = self.images[index] 152 | 153 | for i in range(0, len(ray_dirs), self.batch_size): 154 | yield ray_dirs[i:i+self.batch_size], ray_oris[i:i+self.batch_size], img_pixels[i:i+self.batch_size] 155 | 156 | 157 | def get_rotate_360_rays(self): 158 | def trans_t(t): 159 | return np.array([ 160 | [1,0,0,0], 161 | [0,1,0,0], 162 | [0,0,1,t], 163 | [0,0,0,1], 164 | ], dtype=np.float32) 165 | 166 | def rot_phi(phi): 167 | return np.array([ 168 | [1,0,0,0], 169 | [0,np.cos(phi),-np.sin(phi),0], 170 | [0,np.sin(phi), np.cos(phi),0], 171 | [0,0,0,1], 172 | ], dtype=np.float32) 173 | 174 | def rot_theta(th) : 175 | return np.array([ 176 | [np.cos(th),0,-np.sin(th),0], 177 | [0,1,0,0], 178 | [np.sin(th),0, np.cos(th),0], 179 | [0,0,0,1], 180 | ], dtype=np.float32) 181 | 182 | def pose_spherical(theta, phi, radius): 183 | c2w = trans_t(radius) 184 | c2w = rot_phi(phi/180.*np.pi) @ c2w 185 | c2w = rot_theta(theta/180.*np.pi) @ c2w 186 | c2w = np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]]) @ c2w 187 | return c2w 188 | 189 | for th in np.linspace(-180., 180., 41, endpoint=False): 190 | pose = torch.cuda.FloatTensor(pose_spherical(th, -30., 4.), device=self.device) 191 | 192 | def genfunc(): 193 | ray_dirs, ray_origins = self.make_rays(self.transformed_x, self.transformed_y, pose) 194 | for i in range(0, len(ray_dirs), 1024): 195 | yield ray_dirs[i:i+1024], ray_origins[i:i+1024] 196 | 197 | yield genfunc 198 | 199 | 200 | # Hierarchical sampling (section 5.2) 201 | def sample_pdf(bins, weights, N_samples, det=False): 202 | # Get pdf 203 | device = weights.device 204 | weights = weights + 1e-5 # prevent nans 205 | pdf = weights / torch.sum(weights, -1, keepdim=True) 206 | cdf = torch.cumsum(pdf, -1) 207 | cdf = torch.cat([torch.zeros_like(cdf[...,:1]), cdf], -1) # (batch, len(bins)) 208 | 209 | # Take uniform samples 210 | if det: 211 | u = torch.linspace(0., 1., steps=N_samples, device=device) 212 | u = u.expand(list(cdf.shape[:-1]) + [N_samples]) 213 | else: 214 | u = torch.rand(list(cdf.shape[:-1]) + [N_samples]) 215 | 216 | # Invert CDF 217 | u = u.contiguous() 218 | inds = torch.searchsorted(cdf, u, right=True) 219 | below = torch.max(torch.zeros_like(inds-1), inds-1) 220 | above = torch.min((cdf.shape[-1]-1) * torch.ones_like(inds), inds) 221 | inds_g = torch.stack([below, above], -1) # (batch, N_samples, 2) 222 | 223 | # cdf_g = tf.gather(cdf, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2) 224 | # bins_g = tf.gather(bins, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2) 225 | matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]] 226 | cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g) 227 | bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g) 228 | 229 | denom = (cdf_g[...,1]-cdf_g[...,0]) 230 | denom = torch.where(denom<1e-5, torch.ones_like(denom), denom) 231 | t = (u-cdf_g[...,0])/denom 232 | samples = bins_g[...,0] + t * (bins_g[...,1]-bins_g[...,0]) 233 | return samples 234 | 235 | 236 | def sample_rays(ray_directions, ray_origins, sample_z_vals): 237 | 238 | nrays = len(ray_origins) 239 | sample_z_vals = sample_z_vals.repeat(nrays, 1) 240 | rays = ray_origins[:, None, :] + ray_directions[:, None, :] * sample_z_vals[..., None] 241 | return rays, sample_z_vals 242 | 243 | 244 | def sample_viewdirs(ray_directions): 245 | return ray_directions / torch.norm(ray_directions, dim=-1, keepdim=True) 246 | 247 | 248 | def predict_to_rgb(sigma, rgb, z_vals, raydirs, white_background=False): 249 | 250 | device = sigma.device 251 | delta_prefix = z_vals[..., 1:] - z_vals[..., :-1] 252 | delta_addition = torch.full((z_vals.size(0), 1), 1e10, device=device) 253 | delta = torch.cat([delta_prefix, delta_addition], dim=-1) 254 | delta = delta * torch.norm(raydirs[..., None, :], dim=-1) 255 | 256 | alpha = 1.0 - torch.exp(-sigma * delta) 257 | exp_term = 1.0 - alpha 258 | epsilon = 1e-10 259 | exp_addition = torch.ones(exp_term.size(0), 1, device=device) 260 | exp_term = torch.cat([exp_addition, exp_term + epsilon], dim=-1) 261 | transmittance = torch.cumprod(exp_term, axis=-1)[..., :-1] 262 | 263 | weights = alpha * transmittance 264 | rgb = torch.sum(weights[..., None] * rgb, dim=-2) 265 | depth = torch.sum(weights * z_vals, dim=-1) 266 | acc_map = torch.sum(weights, -1) 267 | 268 | if white_background: 269 | rgb = rgb + (1.0 - acc_map[..., None]) 270 | return rgb, depth, acc_map, weights 271 | 272 | 273 | def render_rays(model, fine, raydirs, rayoris, sample_z_vals, importance=0, white_background=False): 274 | 275 | rays, z_vals = sample_rays(raydirs, rayoris, sample_z_vals) 276 | view_dirs = sample_viewdirs(raydirs) 277 | 278 | sigma, rgb = model(rays, view_dirs) 279 | sigma = sigma.squeeze(dim=-1) 280 | rgb1, depth1, acc_map1, weights1 = predict_to_rgb(sigma, rgb, z_vals, raydirs, white_background) 281 | 282 | # 使用weights1进行重采样 283 | z_vals_mid = .5 * (z_vals[...,1:] + z_vals[...,:-1]) 284 | z_samples = sample_pdf(z_vals_mid, weights1[...,1:-1], importance, det=True) 285 | z_samples = z_samples.detach() 286 | 287 | z_vals, _ = torch.sort(torch.cat([z_vals, z_samples], -1), -1) 288 | rays = rayoris[...,None,:] + raydirs[...,None,:] * z_vals[...,:,None] # [N_rays, N_samples + N_importance, 3] 289 | sigma, rgb = fine(rays, view_dirs) 290 | sigma = sigma.squeeze(dim=-1) 291 | 292 | # 第二次重采样的预测才是最终结果，这是论文中，分层采样环节（Hierarchical sampling） 293 | rgb2, depth2, acc_map2, weights2 = predict_to_rgb(sigma, rgb, z_vals, raydirs, white_background) 294 | return rgb1, rgb2 295 | 296 | # 无视图独立性的head 297 | class NoViewDirHead(nn.Module): 298 | def __init__(self, ninput, noutput): 299 | super().__init__() 300 | 301 | self.head = nn.Linear(ninput, noutput) 302 | 303 | def forward(self, x, view_dirs): 304 | 305 | x = self.head(x) 306 | rgb = x[..., :3].sigmoid() 307 | sigma = x[..., 3].relu() 308 | return sigma, rgb 309 | 310 | # 视图独立性的head 311 | class ViewDenepdentHead(nn.Module): 312 | def __init__(self, ninput, nview): 313 | super().__init__() 314 | 315 | self.feature = nn.Linear(ninput, ninput) 316 | self.view_fc = nn.Linear(ninput + nview, ninput // 2) 317 | self.alpha = nn.Linear(ninput, 1) 318 | self.rgb = nn.Linear(ninput // 2, 3) 319 | 320 | def forward(self, x, view_dirs): 321 | 322 | feature = self.feature(x) 323 | sigma = self.alpha(x).relu() 324 | feature = torch.cat([feature, view_dirs], dim=-1) 325 | feature = self.view_fc(feature).relu() 326 | rgb = self.rgb(feature).sigmoid() 327 | return sigma, rgb 328 | 329 | # 位置编码实现 330 | class Embedder(nn.Module): 331 | def __init__(self, positional_encoding_dim): 332 | super().__init__() 333 | self.positional_encoding_dim = positional_encoding_dim 334 | 335 | def forward(self, x): 336 | 337 | positions = [x] 338 | for i in range(self.positional_encoding_dim): 339 | for fn in [torch.sin, torch.cos]: 340 | positions.append(fn((2.0 ** i) * x)) 341 | 342 | return torch.cat(positions, dim=-1) 343 | 344 | # 基本模型结构 345 | class NeRF(nn.Module): 346 | def __init__(self, x_pedim=10, nwidth=256, ndepth=8, view_pedim=4): 347 | super().__init__() 348 | 349 | xdim = (x_pedim * 2 + 1) * 3 350 | 351 | layers = [] 352 | layers_in = [nwidth] * ndepth 353 | layers_in[0] = xdim 354 | layers_in[5] = nwidth + xdim 355 | 356 | # 模型中特定层[5]会存在concat 357 | for i in range(ndepth): 358 | layers.append(nn.Linear(layers_in[i], nwidth)) 359 | 360 | if view_pedim > 0: 361 | view_dim = (view_pedim * 2 + 1) * 3 362 | self.view_embed = Embedder(view_pedim) 363 | self.head = ViewDenepdentHead(nwidth, view_dim) 364 | else: 365 | self.head = NoViewDirHead(nwidth, 4) 366 | 367 | self.xembed = Embedder(x_pedim) 368 | self.layers = nn.Sequential(*layers) 369 | 370 | 371 | def forward(self, x, view_dirs): 372 | 373 | xshape = x.shape 374 | x = self.xembed(x) 375 | if self.view_embed is not None: 376 | view_dirs = view_dirs[:, None].expand(xshape) 377 | view_dirs = self.view_embed(view_dirs) 378 | 379 | raw_x = x 380 | for i, layer in enumerate(self.layers): 381 | x = torch.relu(layer(x)) 382 | 383 | if i == 4: 384 | x = torch.cat([x, raw_x], axis=-1) 385 | 386 | return self.head(x, view_dirs) 387 | 388 | 389 | def train(): 390 | 391 | pbar = tqdm(range(1, maxiters)) 392 | for global_step in pbar: 393 | 394 | idx = np.random.randint(0, len(trainset)) 395 | raydirs, rayoris, imagepixels = trainset[idx] 396 | 397 | rgb1, rgb2 = render_rays(model, fine, raydirs, rayoris, sample_z_vals, importance, white_background) 398 | loss1 = ((rgb1 - imagepixels)**2).mean() 399 | loss2 = ((rgb2 - imagepixels)**2).mean() 400 | psnr = -10. * torch.log(loss2.detach()) / np.log(10.) 401 | loss = loss1 + loss2 402 | 403 | optimizer.zero_grad() 404 | loss.backward() 405 | optimizer.step() 406 | pbar.set_description(f"{global_step} / {maxiters}, Loss: {loss.item():.6f}, PSNR: {psnr.item():.6f}") 407 | 408 | decay_rate = 0.1 409 | new_lrate = lrate * (decay_rate ** (global_step / lrate_decay)) 410 | for param_group in optimizer.param_groups: 411 | param_group['lr'] = new_lrate 412 | 413 | if global_step % 5000 == 0 or global_step == 500: 414 | 415 | imgpath = f"imgs/{global_step:02d}.png" 416 | pthpath = f"ckpt/{global_step:02d}.pth" 417 | model.eval() 418 | with torch.no_grad(): 419 | rgbs, imgpixels = [], [] 420 | for raydirs, rayoris, imagepixels in trainset.get_test_item(): 421 | 422 | rgb1, rgb2 = render_rays(model, fine, raydirs, rayoris, sample_z_vals, importance, white_background) 423 | rgbs.append(rgb2) 424 | imgpixels.append(imagepixels) 425 | 426 | rgb = torch.cat(rgbs, dim=0) 427 | imgpixels = torch.cat(imgpixels, dim=0) 428 | loss = ((rgb - imgpixels)**2).mean() 429 | psnr = -10. * torch.log(loss) / np.log(10.) 430 | 431 | print(f"Save image {imgpath}, Loss: {loss.item():.6f}, PSNR: {psnr.item():.6f}") 432 | model.train() 433 | 434 | temp_image = (rgb.view(height, width, 3).cpu().numpy() * 255).astype(np.uint8) 435 | cv2.imwrite(imgpath, temp_image[..., ::-1]) 436 | torch.save([model.state_dict(), fine.state_dict()], pthpath) 437 | 438 | 439 | def make_video360(): 440 | 441 | mstate, fstate = torch.load(args.ckpt, map_location="cpu") 442 | model.load_state_dict(mstate) 443 | fine.load_state_dict(fstate) 444 | model.eval() 445 | fine.eval() 446 | imagelist = [] 447 | 448 | for i, gfn in tqdm(enumerate(trainset.get_rotate_360_rays()), desc="Rendering"): 449 | 450 | with torch.no_grad(): 451 | rgbs = [] 452 | for raydirs, rayoris in gfn(): 453 | rgb1, rgb2 = render_rays(model, fine, raydirs, rayoris, sample_z_vals, importance, white_background) 454 | rgbs.append(rgb2) 455 | 456 | rgb = torch.cat(rgbs, dim=0) 457 | 458 | rgb = (rgb.view(height, width, 3).cpu().numpy() * 255).astype(np.uint8) 459 | file = f"rotate360/{i:03d}.png" 460 | 461 | print(f"Rendering to {file}") 462 | cv2.imwrite(file, rgb[..., ::-1]) 463 | imagelist.append(rgb) 464 | 465 | video_file = f"videos/rotate360.mp4" 466 | print(f"Write imagelist to video file {video_file}") 467 | imageio.mimwrite(video_file, imagelist, fps=30, quality=10) 468 | 469 | 470 | if __name__ == "__main__": 471 | 472 | parser = argparse.ArgumentParser() 473 | parser.add_argument("--datadir", type=str, default='data/nerf_synthetic/lego', help='input data directory') 474 | parser.add_argument("--make-video360", action="store_true", help="make 360 rotation video") 475 | parser.add_argument("--half-resolution", action="store_true", help="use half resolution") 476 | parser.add_argument("--ckpt", default="300000.pth", type=str, help="model file used to make 360 rotation video") 477 | args = parser.parse_args() 478 | 479 | device = "cuda:0" 480 | maxiters = 100000 + 1 481 | batch_size = 1024 482 | lrate_decay = 500 * 1000 483 | lrate = 5e-4 484 | importance = 128 485 | num_samples = 64 # 每个光线的采样数量 486 | positional_encoding_dim = 10 # 位置编码维度 487 | view_encoding_dim = 4 # View Dependent对应的位置编码维度 488 | white_background = True # 图片背景是白色的 489 | half_resolution = args.half_resolution # 只进行一半分辨率的重建(400x400)，False表示(800x800)分辨率 490 | sample_z_vals = torch.linspace(2.0, 6.0, num_samples, device=device).view(1, num_samples) 491 | 492 | model = NeRF( 493 | x_pedim = positional_encoding_dim, 494 | view_pedim = view_encoding_dim 495 | ).to(device) 496 | params = list(model.parameters()) 497 | 498 | # 使用model产生的权重进行重采样，然后再推理，所以这个才是效果更好的模型 499 | fine = NeRF( 500 | x_pedim = positional_encoding_dim, 501 | view_pedim = view_encoding_dim 502 | ).to(device) 503 | params.extend(list(fine.parameters())) 504 | 505 | optimizer = optim.Adam(params, lrate) 506 | os.makedirs("imgs", exist_ok=True) 507 | os.makedirs("rotate360", exist_ok=True) 508 | os.makedirs("videos", exist_ok=True) 509 | os.makedirs("ckpt", exist_ok=True) 510 | 511 | print(model) 512 | 513 | provider = BlenderProvider("data/nerf_synthetic/lego", "transforms_train.json", half_resolution) 514 | trainset = NeRFDataset(provider, batch_size, device) 515 | width = trainset.width 516 | height = trainset.height 517 | 518 | if args.make_video360: 519 | make_video360() 520 | else: 521 | train() 522 | 523 | print("Program done.") --------------------------------------------------------------------------------