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train.py
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train.py
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import os, sys
from opt import get_opts
import torch
from collections import defaultdict
from torch.utils.data import DataLoader
from datasets import dataset_dict
# models
from models.nerf import Embedding, NeRF
from models.rendering import render_rays
# optimizer, scheduler, visualization
from utils import *
# losses
from losses import loss_dict
# metrics
from metrics import *
# pytorch-lightning
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.logging import TestTubeLogger
class NeRFSystem(LightningModule):
def __init__(self, hparams):
super(NeRFSystem, self).__init__()
self.hparams = hparams
self.loss = loss_dict[hparams.loss_type]()
self.embedding_xyz = Embedding(3, 10) # 10 is the default number
self.embedding_dir = Embedding(3, 4) # 4 is the default number
self.embeddings = [self.embedding_xyz, self.embedding_dir]
self.nerf_coarse = NeRF()
self.models = [self.nerf_coarse]
if hparams.N_importance > 0:
self.nerf_fine = NeRF()
self.models += [self.nerf_fine]
def decode_batch(self, batch):
rays = batch['rays'] # (B, 8)
rgbs = batch['rgbs'] # (B, 3)
return rays, rgbs
def forward(self, rays):
"""Do batched inference on rays using chunk."""
B = rays.shape[0]
results = defaultdict(list)
for i in range(0, B, self.hparams.chunk):
rendered_ray_chunks = \
render_rays(self.models,
self.embeddings,
rays[i:i+self.hparams.chunk],
self.hparams.N_samples,
self.hparams.use_disp,
self.hparams.perturb,
self.hparams.noise_std,
self.hparams.N_importance,
self.hparams.chunk, # chunk size is effective in val mode
self.train_dataset.white_back)
for k, v in rendered_ray_chunks.items():
results[k] += [v]
for k, v in results.items():
results[k] = torch.cat(v, 0)
return results
def prepare_data(self):
dataset = dataset_dict[self.hparams.dataset_name]
kwargs = {'root_dir': self.hparams.root_dir,
'img_wh': tuple(self.hparams.img_wh)}
if self.hparams.dataset_name == 'llff':
kwargs['spheric_poses'] = self.hparams.spheric_poses
kwargs['val_num'] = self.hparams.num_gpus
self.train_dataset = dataset(split='train', **kwargs)
self.val_dataset = dataset(split='val', **kwargs)
def configure_optimizers(self):
self.optimizer = get_optimizer(self.hparams, self.models)
scheduler = get_scheduler(self.hparams, self.optimizer)
return [self.optimizer], [scheduler]
def train_dataloader(self):
return DataLoader(self.train_dataset,
shuffle=True,
num_workers=4,
batch_size=self.hparams.batch_size,
pin_memory=True)
def val_dataloader(self):
return DataLoader(self.val_dataset,
shuffle=False,
num_workers=4,
batch_size=1, # validate one image (H*W rays) at a time
pin_memory=True)
def training_step(self, batch, batch_nb):
log = {'lr': get_learning_rate(self.optimizer)}
rays, rgbs = self.decode_batch(batch)
results = self(rays)
log['train/loss'] = loss = self.loss(results, rgbs)
typ = 'fine' if 'rgb_fine' in results else 'coarse'
with torch.no_grad():
psnr_ = psnr(results[f'rgb_{typ}'], rgbs)
log['train/psnr'] = psnr_
return {'loss': loss,
'progress_bar': {'train_psnr': psnr_},
'log': log
}
def validation_step(self, batch, batch_nb):
rays, rgbs = self.decode_batch(batch)
rays = rays.squeeze() # (H*W, 3)
rgbs = rgbs.squeeze() # (H*W, 3)
results = self(rays)
log = {'val_loss': self.loss(results, rgbs)}
typ = 'fine' if 'rgb_fine' in results else 'coarse'
if batch_nb == 0:
W, H = self.hparams.img_wh
img = results[f'rgb_{typ}'].view(H, W, 3).cpu()
img = img.permute(2, 0, 1) # (3, H, W)
img_gt = rgbs.view(H, W, 3).permute(2, 0, 1).cpu() # (3, H, W)
depth = visualize_depth(results[f'depth_{typ}'].view(H, W)) # (3, H, W)
stack = torch.stack([img_gt, img, depth]) # (3, 3, H, W)
self.logger.experiment.add_images('val/GT_pred_depth',
stack, self.global_step)
log['val_psnr'] = psnr(results[f'rgb_{typ}'], rgbs)
return log
def validation_epoch_end(self, outputs):
mean_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
mean_psnr = torch.stack([x['val_psnr'] for x in outputs]).mean()
return {'progress_bar': {'val_loss': mean_loss,
'val_psnr': mean_psnr},
'log': {'val/loss': mean_loss,
'val/psnr': mean_psnr}
}
if __name__ == '__main__':
hparams = get_opts()
system = NeRFSystem(hparams)
checkpoint_callback = ModelCheckpoint(filepath=os.path.join(f'ckpts/{hparams.exp_name}',
'{epoch:d}'),
monitor='val/loss',
mode='min',
save_top_k=5,)
logger = TestTubeLogger(
save_dir="logs",
name=hparams.exp_name,
debug=False,
create_git_tag=False
)
trainer = Trainer(max_epochs=hparams.num_epochs,
checkpoint_callback=checkpoint_callback,
resume_from_checkpoint=hparams.ckpt_path,
logger=logger,
early_stop_callback=None,
weights_summary=None,
progress_bar_refresh_rate=1,
gpus=hparams.num_gpus,
distributed_backend='ddp' if hparams.num_gpus>1 else None,
num_sanity_val_steps=1,
benchmark=True,
profiler=hparams.num_gpus==1)
trainer.fit(system)