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train.py
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train.py
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import torch
from torch import nn
from opt import get_opts
import os
import glob
import imageio
import numpy as np
import cv2
from einops import rearrange
# data
from torch.utils.data import DataLoader
from datasets import dataset_dict
from datasets.ray_utils import axisangle_to_R, get_rays
# models
from kornia.utils.grid import create_meshgrid3d
from models.networks import NGP
from models.rendering import render, MAX_SAMPLES
# optimizer, losses
from apex.optimizers import FusedAdam
from torch.optim.lr_scheduler import CosineAnnealingLR
from losses import NeRFLoss
# metrics
from torchmetrics import (
PeakSignalNoiseRatio,
StructuralSimilarityIndexMeasure
)
from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
# pytorch-lightning
from pytorch_lightning.plugins import DDPPlugin
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.callbacks import TQDMProgressBar, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available
from utils import slim_ckpt, load_ckpt
import warnings; warnings.filterwarnings("ignore")
def depth2img(depth):
depth = (depth-depth.min())/(depth.max()-depth.min())
depth_img = cv2.applyColorMap((depth*255).astype(np.uint8),
cv2.COLORMAP_TURBO)
return depth_img
class NeRFSystem(LightningModule):
def __init__(self, hparams):
super().__init__()
self.save_hyperparameters(hparams)
self.warmup_steps = 256
self.update_interval = 16
self.loss = NeRFLoss(lambda_distortion=self.hparams.distortion_loss_w)
self.train_psnr = PeakSignalNoiseRatio(data_range=1)
self.val_psnr = PeakSignalNoiseRatio(data_range=1)
self.val_ssim = StructuralSimilarityIndexMeasure(data_range=1)
if self.hparams.eval_lpips:
self.val_lpips = LearnedPerceptualImagePatchSimilarity('vgg')
for p in self.val_lpips.net.parameters():
p.requires_grad = False
rgb_act = 'None' if self.hparams.use_exposure else 'Sigmoid'
self.model = NGP(scale=self.hparams.scale, rgb_act=rgb_act)
G = self.model.grid_size
self.model.register_buffer('density_grid',
torch.zeros(self.model.cascades, G**3))
self.model.register_buffer('grid_coords',
create_meshgrid3d(G, G, G, False, dtype=torch.int32).reshape(-1, 3))
def forward(self, batch, split):
if split=='train':
poses = self.poses[batch['img_idxs']]
directions = self.directions[batch['pix_idxs']]
else:
poses = batch['pose']
directions = self.directions
if self.hparams.optimize_ext:
dR = axisangle_to_R(self.dR[batch['img_idxs']])
poses[..., :3] = dR @ poses[..., :3]
poses[..., 3] += self.dT[batch['img_idxs']]
rays_o, rays_d = get_rays(directions, poses)
kwargs = {'test_time': split!='train',
'random_bg': self.hparams.random_bg}
if self.hparams.scale > 0.5:
kwargs['exp_step_factor'] = 1/256
if self.hparams.use_exposure:
kwargs['exposure'] = batch['exposure']
return render(self.model, rays_o, rays_d, **kwargs)
def setup(self, stage):
dataset = dataset_dict[self.hparams.dataset_name]
kwargs = {'root_dir': self.hparams.root_dir,
'downsample': self.hparams.downsample}
self.train_dataset = dataset(split=self.hparams.split, **kwargs)
self.train_dataset.batch_size = self.hparams.batch_size
self.train_dataset.ray_sampling_strategy = self.hparams.ray_sampling_strategy
self.test_dataset = dataset(split='test', **kwargs)
def configure_optimizers(self):
# define additional parameters
self.register_buffer('directions', self.train_dataset.directions.to(self.device))
self.register_buffer('poses', self.train_dataset.poses.to(self.device))
if self.hparams.optimize_ext:
N = len(self.train_dataset.poses)
self.register_parameter('dR',
nn.Parameter(torch.zeros(N, 3, device=self.device)))
self.register_parameter('dT',
nn.Parameter(torch.zeros(N, 3, device=self.device)))
load_ckpt(self.model, self.hparams.weight_path)
net_params = []
for n, p in self.named_parameters():
if n not in ['dR', 'dT']: net_params += [p]
opts = []
self.net_opt = FusedAdam(net_params, self.hparams.lr, eps=1e-15)
opts += [self.net_opt]
if self.hparams.optimize_ext:
opts += [FusedAdam([self.dR, self.dT], 1e-6)] # learning rate is hard-coded
net_sch = CosineAnnealingLR(self.net_opt,
self.hparams.num_epochs,
self.hparams.lr/30)
return opts, [net_sch]
def train_dataloader(self):
return DataLoader(self.train_dataset,
num_workers=16,
persistent_workers=True,
batch_size=None,
pin_memory=True)
def val_dataloader(self):
return DataLoader(self.test_dataset,
num_workers=8,
batch_size=None,
pin_memory=True)
def on_train_start(self):
self.model.mark_invisible_cells(self.train_dataset.K.to(self.device),
self.poses,
self.train_dataset.img_wh)
def training_step(self, batch, batch_nb, *args):
if self.global_step%self.update_interval == 0:
self.model.update_density_grid(0.01*MAX_SAMPLES/3**0.5,
warmup=self.global_step<self.warmup_steps,
erode=self.hparams.dataset_name=='colmap')
results = self(batch, split='train')
loss_d = self.loss(results, batch)
if self.hparams.use_exposure:
zero_radiance = torch.zeros(1, 3, device=self.device)
unit_exposure_rgb = self.model.log_radiance_to_rgb(zero_radiance,
**{'exposure': torch.ones(1, 1, device=self.device)})
loss_d['unit_exposure'] = \
0.5*(unit_exposure_rgb-self.train_dataset.unit_exposure_rgb)**2
loss = sum(lo.mean() for lo in loss_d.values())
with torch.no_grad():
self.train_psnr(results['rgb'], batch['rgb'])
self.log('lr', self.net_opt.param_groups[0]['lr'])
self.log('train/loss', loss)
# ray marching samples per ray (occupied space on the ray)
self.log('train/rm_s', results['rm_samples']/len(batch['rgb']), True)
# volume rendering samples per ray (stops marching when transmittance drops below 1e-4)
self.log('train/vr_s', results['vr_samples']/len(batch['rgb']), True)
self.log('train/psnr', self.train_psnr, True)
return loss
def on_validation_start(self):
torch.cuda.empty_cache()
if not self.hparams.no_save_test:
self.val_dir = f'results/{self.hparams.dataset_name}/{self.hparams.exp_name}'
os.makedirs(self.val_dir, exist_ok=True)
def validation_step(self, batch, batch_nb):
rgb_gt = batch['rgb']
results = self(batch, split='test')
logs = {}
# compute each metric per image
self.val_psnr(results['rgb'], rgb_gt)
logs['psnr'] = self.val_psnr.compute()
self.val_psnr.reset()
w, h = self.train_dataset.img_wh
rgb_pred = rearrange(results['rgb'], '(h w) c -> 1 c h w', h=h)
rgb_gt = rearrange(rgb_gt, '(h w) c -> 1 c h w', h=h)
self.val_ssim(rgb_pred, rgb_gt)
logs['ssim'] = self.val_ssim.compute()
self.val_ssim.reset()
if self.hparams.eval_lpips:
self.val_lpips(torch.clip(rgb_pred*2-1, -1, 1),
torch.clip(rgb_gt*2-1, -1, 1))
logs['lpips'] = self.val_lpips.compute()
self.val_lpips.reset()
if not self.hparams.no_save_test: # save test image to disk
idx = batch['img_idxs']
rgb_pred = rearrange(results['rgb'].cpu().numpy(), '(h w) c -> h w c', h=h)
rgb_pred = (rgb_pred*255).astype(np.uint8)
depth = depth2img(rearrange(results['depth'].cpu().numpy(), '(h w) -> h w', h=h))
imageio.imsave(os.path.join(self.val_dir, f'{idx:03d}.png'), rgb_pred)
imageio.imsave(os.path.join(self.val_dir, f'{idx:03d}_d.png'), depth)
return logs
def validation_epoch_end(self, outputs):
psnrs = torch.stack([x['psnr'] for x in outputs])
mean_psnr = all_gather_ddp_if_available(psnrs).mean()
self.log('test/psnr', mean_psnr, True)
ssims = torch.stack([x['ssim'] for x in outputs])
mean_ssim = all_gather_ddp_if_available(ssims).mean()
self.log('test/ssim', mean_ssim)
if self.hparams.eval_lpips:
lpipss = torch.stack([x['lpips'] for x in outputs])
mean_lpips = all_gather_ddp_if_available(lpipss).mean()
self.log('test/lpips_vgg', mean_lpips)
def get_progress_bar_dict(self):
# don't show the version number
items = super().get_progress_bar_dict()
items.pop("v_num", None)
return items
if __name__ == '__main__':
hparams = get_opts()
if hparams.val_only and (not hparams.ckpt_path):
raise ValueError('You need to provide a @ckpt_path for validation!')
system = NeRFSystem(hparams)
ckpt_cb = ModelCheckpoint(dirpath=f'ckpts/{hparams.dataset_name}/{hparams.exp_name}',
filename='{epoch:d}',
save_weights_only=True,
every_n_epochs=hparams.num_epochs,
save_on_train_epoch_end=True,
save_top_k=-1)
callbacks = [ckpt_cb, TQDMProgressBar(refresh_rate=1)]
logger = TensorBoardLogger(save_dir=f"logs/{hparams.dataset_name}",
name=hparams.exp_name,
default_hp_metric=False)
trainer = Trainer(max_epochs=hparams.num_epochs,
check_val_every_n_epoch=hparams.num_epochs,
callbacks=callbacks,
logger=logger,
enable_model_summary=False,
accelerator='gpu',
devices=hparams.num_gpus,
strategy=DDPPlugin(find_unused_parameters=False)
if hparams.num_gpus>1 else None,
num_sanity_val_steps=-1 if hparams.val_only else 0,
precision=16)
trainer.fit(system, ckpt_path=hparams.ckpt_path)
if not hparams.val_only: # save slimmed ckpt for the last epoch
ckpt_ = \
slim_ckpt(f'ckpts/{hparams.dataset_name}/{hparams.exp_name}/epoch={hparams.num_epochs-1}.ckpt',
save_poses=hparams.optimize_ext)
torch.save(ckpt_, f'ckpts/{hparams.dataset_name}/{hparams.exp_name}/epoch={hparams.num_epochs-1}_slim.ckpt')
if (not hparams.no_save_test) and \
hparams.dataset_name=='nsvf' and \
'Synthetic' in hparams.root_dir: # save video
imgs = sorted(glob.glob(os.path.join(system.val_dir, '*.png')))
imageio.mimsave(os.path.join(system.val_dir, 'rgb.mp4'),
[imageio.imread(img) for img in imgs[::2]],
fps=30, macro_block_size=1)
imageio.mimsave(os.path.join(system.val_dir, 'depth.mp4'),
[imageio.imread(img) for img in imgs[1::2]],
fps=30, macro_block_size=1)