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utils.py
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utils.py
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from nerf.utils import *
from nerf.utils import Trainer as _Trainer
# for isinstance
from tensoRF.network_cc import NeRFNetwork as CCNeRF
class Trainer(_Trainer):
def __init__(self,
name, # name of this experiment
opt, # extra conf
model, # network
criterion=None, # loss function, if None, assume inline implementation in train_step
optimizer=None, # optimizer
ema_decay=None, # if use EMA, set the decay
lr_scheduler=None, # scheduler
metrics=[], # metrics for evaluation, if None, use val_loss to measure performance, else use the first metric.
local_rank=0, # which GPU am I
world_size=1, # total num of GPUs
device=None, # device to use, usually setting to None is OK. (auto choose device)
mute=False, # whether to mute all print
fp16=False, # amp optimize level
eval_interval=1, # eval once every $ epoch
max_keep_ckpt=2, # max num of saved ckpts in disk
workspace='workspace', # workspace to save logs & ckpts
best_mode='min', # the smaller/larger result, the better
use_loss_as_metric=True, # use loss as the first metric
report_metric_at_train=False, # also report metrics at training
use_checkpoint="latest", # which ckpt to use at init time
use_tensorboardX=True, # whether to use tensorboard for logging
scheduler_update_every_step=False, # whether to call scheduler.step() after every train step
):
self.optimizer_fn = optimizer
self.lr_scheduler_fn = lr_scheduler
super().__init__(name, opt, model, criterion, optimizer, ema_decay, lr_scheduler, metrics, local_rank, world_size, device, mute, fp16, eval_interval, max_keep_ckpt, workspace, best_mode, use_loss_as_metric, report_metric_at_train, use_checkpoint, use_tensorboardX, scheduler_update_every_step)
### ------------------------------
def train_step(self, data):
pred_rgb, gt_rgb, loss = super().train_step(data)
# l1 reg
loss += self.model.density_loss() * self.opt.l1_reg_weight
return pred_rgb, gt_rgb, loss
def train_one_epoch(self, loader):
self.log(f"==> Start Training Epoch {self.epoch}, lr={self.optimizer.param_groups[0]['lr']:.6f} ...")
total_loss = 0
if self.local_rank == 0 and self.report_metric_at_train:
for metric in self.metrics:
metric.clear()
self.model.train()
# distributedSampler: must call set_epoch() to shuffle indices across multiple epochs
# ref: https://pytorch.org/docs/stable/data.html
if self.world_size > 1:
loader.sampler.set_epoch(self.epoch)
if self.local_rank == 0:
pbar = tqdm.tqdm(total=len(loader) * loader.batch_size, bar_format='{desc}: {percentage:3.0f}% {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]')
self.local_step = 0
for data in loader:
# update grid every 16 steps
if self.model.cuda_ray and self.global_step % self.opt.update_extra_interval == 0:
with torch.cuda.amp.autocast(enabled=self.fp16):
self.model.update_extra_state()
self.local_step += 1
self.global_step += 1
self.optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=self.fp16):
preds, truths, loss = self.train_step(data)
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
if self.scheduler_update_every_step:
self.lr_scheduler.step()
loss_val = loss.item()
total_loss += loss_val
if self.local_rank == 0:
if self.report_metric_at_train:
for metric in self.metrics:
metric.update(preds, truths)
if self.use_tensorboardX:
self.writer.add_scalar("train/loss", loss_val, self.global_step)
self.writer.add_scalar("train/lr", self.optimizer.param_groups[0]['lr'], self.global_step)
if self.scheduler_update_every_step:
pbar.set_description(f"loss={loss_val:.4f} ({total_loss/self.local_step:.4f}), lr={self.optimizer.param_groups[0]['lr']:.6f}")
else:
pbar.set_description(f"loss={loss_val:.4f} ({total_loss/self.local_step:.4f})")
pbar.update(loader.batch_size)
# Different from _Trainer!
if self.global_step in self.opt.upsample_model_steps:
# shrink
if self.model.cuda_ray: # and self.global_step == self.opt.upsample_model_steps[0]:
self.model.shrink_model()
# adaptive voxel size from aabb_train
n_vox = self.upsample_resolutions.pop(0) ** 3 # n_voxels
aabb = self.model.aabb_train.cpu().numpy()
vox_size = np.cbrt(np.prod(aabb[3:] - aabb[:3]) / n_vox)
reso = ((aabb[3:] - aabb[:3]) / vox_size).astype(np.int32).tolist()
self.log(f"[INFO] upsample model at step {self.global_step} from {self.model.resolution} to {reso}")
self.model.upsample_model(reso)
# reset optimizer since params changed.
self.optimizer = self.optimizer_fn(self.model)
self.lr_scheduler = self.lr_scheduler_fn(self.optimizer)
if self.ema is not None:
self.ema.update()
average_loss = total_loss / self.local_step
self.stats["loss"].append(average_loss)
if self.local_rank == 0:
pbar.close()
if self.report_metric_at_train:
for metric in self.metrics:
self.log(metric.report(), style="red")
if self.use_tensorboardX:
metric.write(self.writer, self.epoch, prefix="train")
metric.clear()
if not self.scheduler_update_every_step:
if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
self.lr_scheduler.step(average_loss)
else:
self.lr_scheduler.step()
self.log(f"==> Finished Epoch {self.epoch}.")
# [GUI] just train for 16 steps, without any other overhead that may slow down rendering.
def train_gui(self, train_loader, step=16):
self.model.train()
total_loss = torch.tensor([0], dtype=torch.float32, device=self.device)
loader = iter(train_loader)
for _ in range(step):
# mimic an infinite loop dataloader (in case the total dataset is smaller than step)
try:
data = next(loader)
except StopIteration:
loader = iter(train_loader)
data = next(loader)
# mark untrained grid
if self.global_step == 0:
self.model.mark_untrained_grid(train_loader._data.poses, train_loader._data.intrinsics)
self.error_map = train_loader._data.error_map
# update grid every 16 steps
if self.model.cuda_ray and self.global_step % self.opt.update_extra_interval == 0:
with torch.cuda.amp.autocast(enabled=self.fp16):
self.model.update_extra_state()
self.global_step += 1
self.optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=self.fp16):
preds, truths, loss = self.train_step(data)
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
if self.scheduler_update_every_step:
self.lr_scheduler.step()
total_loss += loss.detach()
# Different from _Trainer!
if self.global_step in self.opt.upsample_model_steps:
# shrink
if self.model.cuda_ray:
self.model.shrink_model()
# adaptive voxel size from aabb_train
n_vox = self.upsample_resolutions.pop(0) ** 3 # n_voxels
aabb = self.model.aabb_train.cpu().numpy()
vox_size = np.cbrt(np.prod(aabb[3:] - aabb[:3]) / n_vox)
reso = ((aabb[3:] - aabb[:3]) / vox_size).astype(np.int32).tolist()
self.log(f"[INFO] upsample model at step {self.global_step} from {self.model.resolution} to {reso}")
self.model.upsample_model(reso)
# reset optimizer since params changed.
self.optimizer = self.optimizer_fn(self.model)
self.lr_scheduler = self.lr_scheduler_fn(self.optimizer)
if self.ema is not None:
self.ema.update()
average_loss = total_loss.item() / step
if not self.scheduler_update_every_step:
if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
self.lr_scheduler.step(average_loss)
else:
self.lr_scheduler.step()
outputs = {
'loss': average_loss,
'lr': self.optimizer.param_groups[0]['lr'],
}
return outputs
def save_checkpoint(self, name=None, full=False, best=False, remove_old=True):
if name is None:
name = f'{self.name}_ep{self.epoch:04d}.pth'
state = {
'epoch': self.epoch,
'global_step': self.global_step,
'stats': self.stats,
'resolution': self.model.resolution, # Different from _Trainer!
}
# special case for CCNeRF...
if isinstance(self.model, CCNeRF):
state['rank_vec_density'] = self.model.rank_vec_density[0]
state['rank_mat_density'] = self.model.rank_mat_density[0]
state['rank_vec'] = self.model.rank_vec[0]
state['rank_mat'] = self.model.rank_mat[0]
if self.model.cuda_ray:
state['mean_count'] = self.model.mean_count
state['mean_density'] = self.model.mean_density
if full:
state['optimizer'] = self.optimizer.state_dict()
state['lr_scheduler'] = self.lr_scheduler.state_dict()
state['scaler'] = self.scaler.state_dict()
if self.ema is not None:
state['ema'] = self.ema.state_dict()
if not best:
state['model'] = self.model.state_dict()
file_path = f"{self.ckpt_path}/{name}.pth"
if remove_old:
self.stats["checkpoints"].append(file_path)
if len(self.stats["checkpoints"]) > self.max_keep_ckpt:
old_ckpt = self.stats["checkpoints"].pop(0)
if os.path.exists(old_ckpt):
os.remove(old_ckpt)
torch.save(state, file_path)
else:
if len(self.stats["results"]) > 0:
if self.stats["best_result"] is None or self.stats["results"][-1] < self.stats["best_result"]:
self.log(f"[INFO] New best result: {self.stats['best_result']} --> {self.stats['results'][-1]}")
self.stats["best_result"] = self.stats["results"][-1]
# save ema results
if self.ema is not None:
self.ema.store()
self.ema.copy_to()
state['model'] = self.model.state_dict()
if self.ema is not None:
self.ema.restore()
torch.save(state, self.best_path)
else:
self.log(f"[WARN] no evaluated results found, skip saving best checkpoint.")
def load_checkpoint(self, checkpoint=None, model_only=False):
if checkpoint is None:
checkpoint_list = sorted(glob.glob(f'{self.ckpt_path}/{self.name}_ep*.pth'))
if checkpoint_list:
checkpoint = checkpoint_list[-1]
self.log(f"[INFO] Latest checkpoint is {checkpoint}")
else:
self.log("[WARN] No checkpoint found, model randomly initialized.")
return
checkpoint_dict = torch.load(checkpoint, map_location=self.device)
# if 'model' not in checkpoint_dict:
# # reset resolution
# self.model.upsample_model() # TODO: need to calclate resolution from param size...
# self.optimizer = self.optimizer_fn(self.model)
# self.lr_scheduler = self.lr_scheduler_fn(self.optimizer)
# self.model.load_state_dict(checkpoint_dict)
# self.log("[INFO] loaded model.")
# return
# special case for CCNeRF: model structure should be identical to ckpt...
if isinstance(self.model, CCNeRF):
# print(checkpoint_dict['rank_vec_density'], checkpoint_dict['rank_mat_density'], checkpoint_dict['rank_vec'], checkpoint_dict['rank_mat'])
# very ugly...
self.model = CCNeRF(
rank_vec_density=checkpoint_dict['rank_vec_density'],
rank_mat_density=checkpoint_dict['rank_mat_density'],
rank_vec=checkpoint_dict['rank_vec'],
rank_mat=checkpoint_dict['rank_mat'],
resolution=checkpoint_dict['resolution'],
bound=self.opt.bound,
cuda_ray=self.opt.cuda_ray,
density_scale=1,
min_near=self.opt.min_near,
density_thresh=self.opt.density_thresh,
bg_radius=self.opt.bg_radius,
).to(self.device)
self.log(f"[INFO] ===== re-initialize CCNeRF =====")
self.log(self.model)
else:
self.model.upsample_model(checkpoint_dict['resolution'])
if self.optimizer_fn is not None:
self.optimizer = self.optimizer_fn(self.model)
if self.lr_scheduler_fn is not None:
self.lr_scheduler = self.lr_scheduler_fn(self.optimizer)
missing_keys, unexpected_keys = self.model.load_state_dict(checkpoint_dict['model'], strict=False)
self.log("[INFO] loaded model.")
if len(missing_keys) > 0:
self.log(f"[WARN] missing keys: {missing_keys}")
if len(unexpected_keys) > 0:
self.log(f"[WARN] unexpected keys: {unexpected_keys}")
if self.ema is not None and 'ema' in checkpoint_dict:
self.ema.load_state_dict(checkpoint_dict['ema'])
if self.model.cuda_ray:
if 'mean_count' in checkpoint_dict:
self.model.mean_count = checkpoint_dict['mean_count']
if 'mean_density' in checkpoint_dict:
self.model.mean_density = checkpoint_dict['mean_density']
if model_only:
return
self.stats = checkpoint_dict['stats']
self.epoch = checkpoint_dict['epoch']
self.global_step = checkpoint_dict['global_step']
self.log(f"[INFO] load at epoch {self.epoch}, global step {self.global_step}")
if self.optimizer and 'optimizer' in checkpoint_dict:
try:
self.optimizer.load_state_dict(checkpoint_dict['optimizer'])
self.log("[INFO] loaded optimizer.")
except:
self.log("[WARN] Failed to load optimizer.")
if self.lr_scheduler and 'lr_scheduler' in checkpoint_dict:
try:
self.lr_scheduler.load_state_dict(checkpoint_dict['lr_scheduler'])
self.log("[INFO] loaded scheduler.")
except:
self.log("[WARN] Failed to load scheduler.")
if self.scaler and 'scaler' in checkpoint_dict:
try:
self.scaler.load_state_dict(checkpoint_dict['scaler'])
self.log("[INFO] loaded scaler.")
except:
self.log("[WARN] Failed to load scaler.")