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
)