Spaces:
Runtime error
Runtime error
File size: 12,310 Bytes
cfb7702 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_efficient_distloss import flatten_eff_distloss
import pytorch_lightning as pl
from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_debug
import models
from models.utils import cleanup
from models.ray_utils import get_rays
import systems
from systems.base import BaseSystem
from systems.criterions import PSNR, binary_cross_entropy
@systems.register('neus-system')
class NeuSSystem(BaseSystem):
"""
Two ways to print to console:
1. self.print: correctly handle progress bar
2. rank_zero_info: use the logging module
"""
def prepare(self):
self.criterions = {
'psnr': PSNR()
}
self.train_num_samples = self.config.model.train_num_rays * (self.config.model.num_samples_per_ray + self.config.model.get('num_samples_per_ray_bg', 0))
self.train_num_rays = self.config.model.train_num_rays
def forward(self, batch):
return self.model(batch['rays'])
def preprocess_data(self, batch, stage):
if 'index' in batch: # validation / testing
index = batch['index']
else:
if self.config.model.batch_image_sampling:
index = torch.randint(0, len(self.dataset.all_images), size=(self.train_num_rays,), device=self.dataset.all_images.device)
else:
index = torch.randint(0, len(self.dataset.all_images), size=(1,), device=self.dataset.all_images.device)
if stage in ['train']:
c2w = self.dataset.all_c2w[index]
x = torch.randint(
0, self.dataset.w, size=(self.train_num_rays,), device=self.dataset.all_images.device
)
y = torch.randint(
0, self.dataset.h, size=(self.train_num_rays,), device=self.dataset.all_images.device
)
if self.dataset.directions.ndim == 3: # (H, W, 3)
directions = self.dataset.directions[y, x]
elif self.dataset.directions.ndim == 4: # (N, H, W, 3)
directions = self.dataset.directions[index, y, x]
rays_o, rays_d = get_rays(directions, c2w)
rgb = self.dataset.all_images[index, y, x].view(-1, self.dataset.all_images.shape[-1]).to(self.rank)
fg_mask = self.dataset.all_fg_masks[index, y, x].view(-1).to(self.rank)
else:
c2w = self.dataset.all_c2w[index][0]
if self.dataset.directions.ndim == 3: # (H, W, 3)
directions = self.dataset.directions
elif self.dataset.directions.ndim == 4: # (N, H, W, 3)
directions = self.dataset.directions[index][0]
rays_o, rays_d = get_rays(directions, c2w)
rgb = self.dataset.all_images[index].view(-1, self.dataset.all_images.shape[-1]).to(self.rank)
fg_mask = self.dataset.all_fg_masks[index].view(-1).to(self.rank)
rays = torch.cat([rays_o, F.normalize(rays_d, p=2, dim=-1)], dim=-1)
if stage in ['train']:
if self.config.model.background_color == 'white':
self.model.background_color = torch.ones((3,), dtype=torch.float32, device=self.rank)
elif self.config.model.background_color == 'random':
self.model.background_color = torch.rand((3,), dtype=torch.float32, device=self.rank)
else:
raise NotImplementedError
else:
self.model.background_color = torch.ones((3,), dtype=torch.float32, device=self.rank)
if self.dataset.apply_mask:
rgb = rgb * fg_mask[...,None] + self.model.background_color * (1 - fg_mask[...,None])
batch.update({
'rays': rays,
'rgb': rgb,
'fg_mask': fg_mask
})
def training_step(self, batch, batch_idx):
out = self(batch)
loss = 0.
# update train_num_rays
if self.config.model.dynamic_ray_sampling:
train_num_rays = int(self.train_num_rays * (self.train_num_samples / out['num_samples_full'].sum().item()))
self.train_num_rays = min(int(self.train_num_rays * 0.9 + train_num_rays * 0.1), self.config.model.max_train_num_rays)
loss_rgb_mse = F.mse_loss(out['comp_rgb_full'][out['rays_valid_full'][...,0]], batch['rgb'][out['rays_valid_full'][...,0]])
self.log('train/loss_rgb_mse', loss_rgb_mse)
loss += loss_rgb_mse * self.C(self.config.system.loss.lambda_rgb_mse)
loss_rgb_l1 = F.l1_loss(out['comp_rgb_full'][out['rays_valid_full'][...,0]], batch['rgb'][out['rays_valid_full'][...,0]])
self.log('train/loss_rgb', loss_rgb_l1)
loss += loss_rgb_l1 * self.C(self.config.system.loss.lambda_rgb_l1)
loss_eikonal = ((torch.linalg.norm(out['sdf_grad_samples'], ord=2, dim=-1) - 1.)**2).mean()
self.log('train/loss_eikonal', loss_eikonal)
loss += loss_eikonal * self.C(self.config.system.loss.lambda_eikonal)
opacity = torch.clamp(out['opacity'].squeeze(-1), 1.e-3, 1.-1.e-3)
loss_mask = binary_cross_entropy(opacity, batch['fg_mask'].float())
self.log('train/loss_mask', loss_mask)
loss += loss_mask * (self.C(self.config.system.loss.lambda_mask) if self.dataset.has_mask else 0.0)
loss_opaque = binary_cross_entropy(opacity, opacity)
self.log('train/loss_opaque', loss_opaque)
loss += loss_opaque * self.C(self.config.system.loss.lambda_opaque)
loss_sparsity = torch.exp(-self.config.system.loss.sparsity_scale * out['sdf_samples'].abs()).mean()
self.log('train/loss_sparsity', loss_sparsity)
loss += loss_sparsity * self.C(self.config.system.loss.lambda_sparsity)
if self.C(self.config.system.loss.lambda_curvature) > 0:
assert 'sdf_laplace_samples' in out, "Need geometry.grad_type='finite_difference' to get SDF Laplace samples"
loss_curvature = out['sdf_laplace_samples'].abs().mean()
self.log('train/loss_curvature', loss_curvature)
loss += loss_curvature * self.C(self.config.system.loss.lambda_curvature)
# distortion loss proposed in MipNeRF360
# an efficient implementation from https://github.com/sunset1995/torch_efficient_distloss
if self.C(self.config.system.loss.lambda_distortion) > 0:
loss_distortion = flatten_eff_distloss(out['weights'], out['points'], out['intervals'], out['ray_indices'])
self.log('train/loss_distortion', loss_distortion)
loss += loss_distortion * self.C(self.config.system.loss.lambda_distortion)
if self.config.model.learned_background and self.C(self.config.system.loss.lambda_distortion_bg) > 0:
loss_distortion_bg = flatten_eff_distloss(out['weights_bg'], out['points_bg'], out['intervals_bg'], out['ray_indices_bg'])
self.log('train/loss_distortion_bg', loss_distortion_bg)
loss += loss_distortion_bg * self.C(self.config.system.loss.lambda_distortion_bg)
losses_model_reg = self.model.regularizations(out)
for name, value in losses_model_reg.items():
self.log(f'train/loss_{name}', value)
loss_ = value * self.C(self.config.system.loss[f"lambda_{name}"])
loss += loss_
self.log('train/inv_s', out['inv_s'], prog_bar=True)
for name, value in self.config.system.loss.items():
if name.startswith('lambda'):
self.log(f'train_params/{name}', self.C(value))
self.log('train/num_rays', float(self.train_num_rays), prog_bar=True)
return {
'loss': loss
}
"""
# aggregate outputs from different devices (DP)
def training_step_end(self, out):
pass
"""
"""
# aggregate outputs from different iterations
def training_epoch_end(self, out):
pass
"""
def validation_step(self, batch, batch_idx):
out = self(batch)
psnr = self.criterions['psnr'](out['comp_rgb_full'].to(batch['rgb']), batch['rgb'])
W, H = self.dataset.img_wh
self.save_image_grid(f"it{self.global_step}-{batch['index'][0].item()}.png", [
{'type': 'rgb', 'img': batch['rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}},
{'type': 'rgb', 'img': out['comp_rgb_full'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}
] + ([
{'type': 'rgb', 'img': out['comp_rgb_bg'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}},
{'type': 'rgb', 'img': out['comp_rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}},
] if self.config.model.learned_background else []) + [
{'type': 'grayscale', 'img': out['depth'].view(H, W), 'kwargs': {}},
{'type': 'rgb', 'img': out['comp_normal'].view(H, W, 3), 'kwargs': {'data_format': 'HWC', 'data_range': (-1, 1)}}
])
return {
'psnr': psnr,
'index': batch['index']
}
"""
# aggregate outputs from different devices when using DP
def validation_step_end(self, out):
pass
"""
def validation_epoch_end(self, out):
out = self.all_gather(out)
if self.trainer.is_global_zero:
out_set = {}
for step_out in out:
# DP
if step_out['index'].ndim == 1:
out_set[step_out['index'].item()] = {'psnr': step_out['psnr']}
# DDP
else:
for oi, index in enumerate(step_out['index']):
out_set[index[0].item()] = {'psnr': step_out['psnr'][oi]}
psnr = torch.mean(torch.stack([o['psnr'] for o in out_set.values()]))
self.log('val/psnr', psnr, prog_bar=True, rank_zero_only=True)
def test_step(self, batch, batch_idx):
out = self(batch)
psnr = self.criterions['psnr'](out['comp_rgb_full'].to(batch['rgb']), batch['rgb'])
W, H = self.dataset.img_wh
self.save_image_grid(f"it{self.global_step}-test/{batch['index'][0].item()}.png", [
{'type': 'rgb', 'img': batch['rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}},
{'type': 'rgb', 'img': out['comp_rgb_full'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}}
] + ([
{'type': 'rgb', 'img': out['comp_rgb_bg'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}},
{'type': 'rgb', 'img': out['comp_rgb'].view(H, W, 3), 'kwargs': {'data_format': 'HWC'}},
] if self.config.model.learned_background else []) + [
{'type': 'grayscale', 'img': out['depth'].view(H, W), 'kwargs': {}},
{'type': 'rgb', 'img': out['comp_normal'].view(H, W, 3), 'kwargs': {'data_format': 'HWC', 'data_range': (-1, 1)}}
])
return {
'psnr': psnr,
'index': batch['index']
}
def test_epoch_end(self, out):
"""
Synchronize devices.
Generate image sequence using test outputs.
"""
out = self.all_gather(out)
if self.trainer.is_global_zero:
out_set = {}
for step_out in out:
# DP
if step_out['index'].ndim == 1:
out_set[step_out['index'].item()] = {'psnr': step_out['psnr']}
# DDP
else:
for oi, index in enumerate(step_out['index']):
out_set[index[0].item()] = {'psnr': step_out['psnr'][oi]}
psnr = torch.mean(torch.stack([o['psnr'] for o in out_set.values()]))
self.log('test/psnr', psnr, prog_bar=True, rank_zero_only=True)
self.save_img_sequence(
f"it{self.global_step}-test",
f"it{self.global_step}-test",
'(\d+)\.png',
save_format='mp4',
fps=30
)
self.export()
def export(self):
mesh = self.model.export(self.config.export)
self.save_mesh(
f"it{self.global_step}-{self.config.model.geometry.isosurface.method}{self.config.model.geometry.isosurface.resolution}.obj",
**mesh
)
|