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main_baseline.py
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main_baseline.py
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import os, sys
import os.path as osp
import time
from functools import partial
import gc
import traceback
import numpy as np
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import transforms
import datasets
import models
import cmd_args
from main_utils import *
from models import EPE3DLoss
from evaluation_bnn import evaluate
from tqdm import tqdm
torch.distributed.init_process_group(backend="nccl") # distributed .........................
torch.autograd.set_detect_anomaly(True)
def main():
# ensure numba JIT is on
if 'NUMBA_DISABLE_JIT' in os.environ:
del os.environ['NUMBA_DISABLE_JIT']
# parse arguments
global args
args = cmd_args.parse_args_from_yaml(sys.argv[-1])
# args = cmd_args.parse_args_from_yaml('configs/train_ours_gtav_ddp.yaml')
# -------------------- logging args --------------------
if osp.exists(args.ckpt_dir):
to_continue = query_yes_no('Attention!!!, ckpt_dir already exists!\
Whether to continue?',
default=None)
if not to_continue:
sys.exit(1)
os.makedirs(args.ckpt_dir, mode=0o777, exist_ok=True)
logger = Logger(osp.join(args.ckpt_dir, 'log'))
logger.log('sys.argv:\n' + ' '.join(sys.argv))
local_rank = torch.distributed.get_rank()
print('local rank ', local_rank)
torch.cuda.set_device(local_rank)
os.environ['NUMBA_NUM_THREADS'] = str(args.workers)
logger.log('NUMBA NUM THREADS\t' + os.environ['NUMBA_NUM_THREADS'])
for arg in sorted(vars(args)):
logger.log('{:20s} {}'.format(arg, getattr(args, arg)))
logger.log('')
# -------------------- dataset & loader --------------------
if not args.evaluate:
train_dataset = datasets.__dict__[args.dataset](
train=True,
transform=transforms.Augmentation(args.aug_together,
args.aug_pc2,
args.data_process,
args.num_points,
args.allow_less_points),
gen_func=transforms.GenerateDataUnsymmetric(args),
args=args
)
logger.log('train_dataset: ' + str(train_dataset))
'''
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
worker_init_fn=lambda x: np.random.seed((torch.initial_seed()) % (2 ** 32))
)'''
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True,
sampler=train_sampler)
val_dataset = datasets.__dict__[args.val_dataset](
train=False,
transform=transforms.ProcessData(args.data_process,
args.num_points,
args.allow_less_points),
gen_func=transforms.GenerateDataUnsymmetric(args),
args=args
)
logger.log('val_dataset: ' + str(val_dataset))
'''
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
worker_init_fn=lambda x: np.random.seed((torch.initial_seed()) % (2 ** 32))
)'''
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True,
sampler=val_sampler)
# -------------------- create model --------------------
logger.log("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](args)
if not args.evaluate:
init_func = partial(init_weights_multi, init_type=args.init, gain=args.gain)
model.apply(init_func)
logger.log(model)
#model = torch.nn.DataParallel(model).cuda()
model = model.cuda()
device = torch.device('cuda:%d' % local_rank)
model = model.to(device)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank])
criterion = EPE3DLoss().cuda().to(device)
if args.evaluate:
torch.backends.cudnn.enabled = False
else:
cudnn.benchmark = True
# https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
# But if your input sizes changes at each iteration,
# then cudnn will benchmark every time a new size appears,
# possibly leading to worse runtime performances.
# -------------------- resume --------------------
if args.resume:
if osp.isfile(args.resume):
logger.log("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location=device)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'], strict=True)
logger.log("=> loaded checkpoint '{}' (start epoch {}, min loss {})"
.format(args.resume, checkpoint['epoch'], checkpoint['min_loss']))
else:
logger.log("=> no checkpoint found at '{}'".format(args.resume))
checkpoint = None
else:
args.start_epoch = 0
# -------------------- evaluation --------------------
if args.evaluate:
res_str = evaluate(val_loader, model, logger, args)
logger.close()
return res_str
# -------------------- optimizer --------------------
optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr,
weight_decay=0)
if args.resume and (checkpoint is not None):
optimizer.load_state_dict(checkpoint['optimizer'])
if hasattr(args, 'reset_lr') and args.reset_lr:
print('reset lr')
reset_learning_rate(optimizer, args)
# -------------------- main loop --------------------
min_train_loss = None
best_train_epoch = None
best_val_epoch = None
do_eval = True
for epoch in range(args.start_epoch, args.epochs):
old_lr = optimizer.param_groups[0]['lr']
adjust_learning_rate(optimizer, epoch, args)
lr = optimizer.param_groups[0]['lr']
if old_lr != lr:
print('Switch lr!')
logger.log('lr: ' + str(optimizer.param_groups[0]['lr']))
train_loss = train(train_loader, model, criterion, optimizer, epoch, logger)
gc.collect()
is_train_best = True if best_train_epoch is None else (train_loss < min_train_loss)
if is_train_best:
min_train_loss = train_loss
best_train_epoch = epoch
# if do_eval:
if do_eval and ((epoch == 0)|(epoch > 10)):
if epoch % 3 == 0:
val_loss = validate(val_loader, model, criterion, logger)
gc.collect()
is_val_best = True if best_val_epoch is None else (val_loss < min_val_loss)
if is_val_best:
min_val_loss = val_loss
best_val_epoch = epoch
logger.log("New min val loss!")
min_loss = min_val_loss if do_eval else min_train_loss
is_best = is_val_best if do_eval else is_train_best
save_checkpoint({
'epoch': epoch + 1, # next start epoch
'arch': args.arch,
'state_dict': model.state_dict(),
'min_loss': min_loss,
'optimizer': optimizer.state_dict(),
}, is_best, args.ckpt_dir)
train_str = 'Best train loss: {:.5f} at epoch {:3d}'.format(min_train_loss, best_train_epoch)
logger.log(train_str)
if do_eval:
val_str = 'Best val loss: {:.5f} at epoch {:3d}'.format(min_val_loss, best_val_epoch)
logger.log(val_str)
logger.close()
result_str = val_str if do_eval else train_str
return result_str
def train(train_loader, model, criterion, optimizer, epoch, logger):
epe3d_losses = AverageMeter()
total_losses = AverageMeter()
model.train()
for i, (pc1, pc2, sf, generated_data, path) in tqdm(enumerate(train_loader), total=len(train_loader)):
try:
cur_sf = sf.cuda(non_blocking=True)
output = model(pc1, pc2, generated_data)
epe3d_loss = criterion(input=output, target=cur_sf).mean()
optimizer.zero_grad()
epe3d_loss.backward()
optimizer.step()
epe3d_losses.update(epe3d_loss.item(), pc1.size(0)) # batch size can only be 1 for now
if i % args.print_freq == 0:
logger.log('Epoch: [{0}][{1}/{2}]\t'
'EPE3D Loss {epe3d_losses_.val:.4f} ({epe3d_losses_.avg:.4f})'
.format(
epoch + 1, i + 1, len(train_loader),
epe3d_losses_=epe3d_losses), end='')
logger.log('')
except RuntimeError as ex:
logger.log("in TRAIN, RuntimeError " + repr(ex))
logger.log("batch idx: " + str(i) + ' path: ' + path[0])
traceback.print_tb(ex.__traceback__, file=logger.out_fd)
traceback.print_tb(ex.__traceback__)
if "CUDA error: out of memory" in str(ex) or "cuda runtime error" in str(ex):
logger.log("out of memory, continue")
del pc1, pc2, sf, generated_data
if 'output' in locals():
del output
torch.cuda.empty_cache()
gc.collect()
else:
sys.exit(1)
logger.log(
' * Train EPE3D {epe3d_losses_.avg:.4f}'.format(epe3d_losses_=epe3d_losses))
return epe3d_losses.avg
def validate(val_loader, model, criterion, logger):
epe3d_losses = AverageMeter()
model.eval()
with torch.no_grad():
for i, (pc1, pc2, sf, generated_data, path) in enumerate(val_loader):
try:
cur_sf = sf.cuda(non_blocking=True)
output = model(pc1, pc2, generated_data)
epe3d_loss = criterion(input=output, target=cur_sf)
epe3d_losses.update(epe3d_loss.mean().item())
if i % args.print_freq == 0:
logger.log('Test: [{0}/{1}]\t'
'EPE3D loss {epe3d_losses_.val:.4f} ({epe3d_losses_.avg:.4f})'
.format(i + 1, len(val_loader),
epe3d_losses_=epe3d_losses))
except RuntimeError as ex:
logger.log("in VAL, RuntimeError " + repr(ex))
traceback.print_tb(ex.__traceback__, file=logger.out_fd)
traceback.print_tb(ex.__traceback__)
if "CUDA error: out of memory" in str(ex) or "cuda runtime error" in str(ex):
logger.log("out of memory, continue")
del pc1, pc2, sf, generated_data
torch.cuda.empty_cache()
gc.collect()
print('remained objects after OOM crash')
else:
sys.exit(1)
logger.log(' * EPE3D loss {epe3d_loss_.avg:.4f}'.format(epe3d_loss_=epe3d_losses))
return epe3d_losses.avg
if __name__ == '__main__':
main()