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import os
import torch
import tqdm
import time
import glob
from torch.nn.utils import clip_grad_norm_
from pcdet.utils import common_utils, commu_utils
def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, accumulated_iter, optim_cfg,
rank, tbar, total_it_each_epoch, dataloader_iter, tb_log=None, leave_pbar=False,
use_logger_to_record=False, logger=None, logger_iter_interval=50, cur_epoch=None,
total_epochs=None, ckpt_save_dir=None, ckpt_save_time_interval=300, show_gpu_stat=False, use_amp=False):
if total_it_each_epoch == len(train_loader):
dataloader_iter = iter(train_loader)
ckpt_save_cnt = 1
start_it = accumulated_iter % total_it_each_epoch
scaler = torch.cuda.amp.GradScaler(enabled=use_amp, init_scale=optim_cfg.get('LOSS_SCALE_FP16', 2.0**16))
if rank == 0:
pbar = tqdm.tqdm(total=total_it_each_epoch, leave=leave_pbar, desc='train', dynamic_ncols=True)
data_time = common_utils.AverageMeter()
batch_time = common_utils.AverageMeter()
forward_time = common_utils.AverageMeter()
losses_m = common_utils.AverageMeter()
end = time.time()
for cur_it in range(start_it, total_it_each_epoch):
try:
batch = next(dataloader_iter)
except StopIteration:
dataloader_iter = iter(train_loader)
batch = next(dataloader_iter)
print('new iters')
data_timer = time.time()
cur_data_time = data_timer - end
lr_scheduler.step(accumulated_iter, cur_epoch)
try:
cur_lr = float(optimizer.lr)
except:
cur_lr = optimizer.param_groups[0]['lr']
if tb_log is not None:
tb_log.add_scalar('meta_data/learning_rate', cur_lr, accumulated_iter)
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=use_amp):
loss, tb_dict, disp_dict = model_func(model, batch)
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
clip_grad_norm_(model.parameters(), optim_cfg.GRAD_NORM_CLIP)
scaler.step(optimizer)
scaler.update()
accumulated_iter += 1
cur_forward_time = time.time() - data_timer
cur_batch_time = time.time() - end
end = time.time()
# average reduce
avg_data_time = commu_utils.average_reduce_value(cur_data_time)
avg_forward_time = commu_utils.average_reduce_value(cur_forward_time)
avg_batch_time = commu_utils.average_reduce_value(cur_batch_time)
# log to console and tensorboard
if rank == 0:
batch_size = batch.get('batch_size', None)
data_time.update(avg_data_time)
forward_time.update(avg_forward_time)
batch_time.update(avg_batch_time)
losses_m.update(loss.item() , batch_size)
disp_dict.update({
'loss': loss.item(), 'lr': cur_lr, 'd_time': f'{data_time.val:.2f}({data_time.avg:.2f})',
'f_time': f'{forward_time.val:.2f}({forward_time.avg:.2f})', 'b_time': f'{batch_time.val:.2f}({batch_time.avg:.2f})'
})
if use_logger_to_record:
if accumulated_iter % logger_iter_interval == 0 or cur_it == start_it or cur_it + 1 == total_it_each_epoch:
trained_time_past_all = tbar.format_dict['elapsed']
second_each_iter = pbar.format_dict['elapsed'] / max(cur_it - start_it + 1, 1.0)
trained_time_each_epoch = pbar.format_dict['elapsed']
remaining_second_each_epoch = second_each_iter * (total_it_each_epoch - cur_it)
remaining_second_all = second_each_iter * ((total_epochs - cur_epoch) * total_it_each_epoch - cur_it)
logger.info(
'Train: {:>4d}/{} ({:>3.0f}%) [{:>4d}/{} ({:>3.0f}%)] '
'Loss: {loss.val:#.4g} ({loss.avg:#.3g}) '
'LR: {lr:.3e} '
f'Time cost: {tbar.format_interval(trained_time_each_epoch)}/{tbar.format_interval(remaining_second_each_epoch)} '
f'[{tbar.format_interval(trained_time_past_all)}/{tbar.format_interval(remaining_second_all)}] '
'Acc_iter {acc_iter:<10d} '
'Data time: {data_time.val:.2f}({data_time.avg:.2f}) '
'Forward time: {forward_time.val:.2f}({forward_time.avg:.2f}) '
'Batch time: {batch_time.val:.2f}({batch_time.avg:.2f})'.format(
cur_epoch+1,total_epochs, 100. * (cur_epoch+1) / total_epochs,
cur_it,total_it_each_epoch, 100. * cur_it / total_it_each_epoch,
loss=losses_m,
lr=cur_lr,
acc_iter=accumulated_iter,
data_time=data_time,
forward_time=forward_time,
batch_time=batch_time
)
)
if show_gpu_stat and accumulated_iter % (3 * logger_iter_interval) == 0:
# To show the GPU utilization, please install gpustat through "pip install gpustat"
gpu_info = os.popen('gpustat').read()
logger.info(gpu_info)
else:
pbar.update()
pbar.set_postfix(dict(total_it=accumulated_iter))
tbar.set_postfix(disp_dict)
# tbar.refresh()
if tb_log is not None:
tb_log.add_scalar('train/loss', loss, accumulated_iter)
tb_log.add_scalar('meta_data/learning_rate', cur_lr, accumulated_iter)
for key, val in tb_dict.items():
tb_log.add_scalar('train/' + key, val, accumulated_iter)
# save intermediate ckpt every {ckpt_save_time_interval} seconds
time_past_this_epoch = pbar.format_dict['elapsed']
if time_past_this_epoch // ckpt_save_time_interval >= ckpt_save_cnt:
ckpt_name = ckpt_save_dir / 'latest_model'
save_checkpoint(
checkpoint_state(model, optimizer, cur_epoch, accumulated_iter), filename=ckpt_name,
)
logger.info(f'Save latest model to {ckpt_name}')
ckpt_save_cnt += 1
if rank == 0:
pbar.close()
return accumulated_iter
def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_cfg,
start_epoch, total_epochs, start_iter, rank, tb_log, ckpt_save_dir, train_sampler=None,
lr_warmup_scheduler=None, ckpt_save_interval=1, max_ckpt_save_num=50,
merge_all_iters_to_one_epoch=False, use_amp=False,
use_logger_to_record=False, logger=None, logger_iter_interval=None, ckpt_save_time_interval=None, show_gpu_stat=False, cfg=None):
accumulated_iter = start_iter
# use for disable data augmentation hook
hook_config = cfg.get('HOOK', None)
augment_disable_flag = False
with tqdm.trange(start_epoch, total_epochs, desc='epochs', dynamic_ncols=True, leave=(rank == 0)) as tbar:
total_it_each_epoch = len(train_loader)
if merge_all_iters_to_one_epoch:
assert hasattr(train_loader.dataset, 'merge_all_iters_to_one_epoch')
train_loader.dataset.merge_all_iters_to_one_epoch(merge=True, epochs=total_epochs)
total_it_each_epoch = len(train_loader) // max(total_epochs, 1)
dataloader_iter = iter(train_loader)
for cur_epoch in tbar:
if train_sampler is not None:
train_sampler.set_epoch(cur_epoch)
# train one epoch
if lr_warmup_scheduler is not None and cur_epoch < optim_cfg.WARMUP_EPOCH:
cur_scheduler = lr_warmup_scheduler
else:
cur_scheduler = lr_scheduler
augment_disable_flag = disable_augmentation_hook(hook_config, dataloader_iter, total_epochs, cur_epoch, cfg, augment_disable_flag, logger)
accumulated_iter = train_one_epoch(
model, optimizer, train_loader, model_func,
lr_scheduler=cur_scheduler,
accumulated_iter=accumulated_iter, optim_cfg=optim_cfg,
rank=rank, tbar=tbar, tb_log=tb_log,
leave_pbar=(cur_epoch + 1 == total_epochs),
total_it_each_epoch=total_it_each_epoch,
dataloader_iter=dataloader_iter,
cur_epoch=cur_epoch, total_epochs=total_epochs,
use_logger_to_record=use_logger_to_record,
logger=logger, logger_iter_interval=logger_iter_interval,
ckpt_save_dir=ckpt_save_dir, ckpt_save_time_interval=ckpt_save_time_interval,
show_gpu_stat=show_gpu_stat,
use_amp=use_amp
)
# save trained model
trained_epoch = cur_epoch + 1
if trained_epoch % ckpt_save_interval == 0 and rank == 0:
ckpt_list = glob.glob(str(ckpt_save_dir / 'checkpoint_epoch_*.pth'))
ckpt_list.sort(key=os.path.getmtime)
if ckpt_list.__len__() >= max_ckpt_save_num:
for cur_file_idx in range(0, len(ckpt_list) - max_ckpt_save_num + 1):
os.remove(ckpt_list[cur_file_idx])
ckpt_name = ckpt_save_dir / ('checkpoint_epoch_%d' % trained_epoch)
save_checkpoint(
checkpoint_state(model, optimizer, trained_epoch, accumulated_iter), filename=ckpt_name,
)
def model_state_to_cpu(model_state):
model_state_cpu = type(model_state)() # ordered dict
for key, val in model_state.items():
model_state_cpu[key] = val.cpu()
return model_state_cpu
def checkpoint_state(model=None, optimizer=None, epoch=None, it=None):
optim_state = optimizer.state_dict() if optimizer is not None else None
if model is not None:
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
model_state = model_state_to_cpu(model.module.state_dict())
else:
model_state = model.state_dict()
else:
model_state = None
try:
import pcdet
version = 'pcdet+' + pcdet.__version__
except:
version = 'none'
return {'epoch': epoch, 'it': it, 'model_state': model_state, 'optimizer_state': optim_state, 'version': version}
def save_checkpoint(state, filename='checkpoint'):
if False and 'optimizer_state' in state:
optimizer_state = state['optimizer_state']
state.pop('optimizer_state', None)
optimizer_filename = '{}_optim.pth'.format(filename)
if torch.__version__ >= '1.4':
torch.save({'optimizer_state': optimizer_state}, optimizer_filename, _use_new_zipfile_serialization=False)
else:
torch.save({'optimizer_state': optimizer_state}, optimizer_filename)
filename = '{}.pth'.format(filename)
if torch.__version__ >= '1.4':
torch.save(state, filename, _use_new_zipfile_serialization=False)
else:
torch.save(state, filename)
def disable_augmentation_hook(hook_config, dataloader, total_epochs, cur_epoch, cfg, flag, logger):
"""
This hook turns off the data augmentation during training.
"""
if hook_config is not None:
DisableAugmentationHook = hook_config.get('DisableAugmentationHook', None)
if DisableAugmentationHook is not None:
num_last_epochs = DisableAugmentationHook.NUM_LAST_EPOCHS
if (total_epochs - num_last_epochs) <= cur_epoch and not flag:
DISABLE_AUG_LIST = DisableAugmentationHook.DISABLE_AUG_LIST
dataset_cfg=cfg.DATA_CONFIG
logger.info(f'Disable augmentations: {DISABLE_AUG_LIST}')
dataset_cfg.DATA_AUGMENTOR.DISABLE_AUG_LIST = DISABLE_AUG_LIST
dataloader._dataset.data_augmentor.disable_augmentation(dataset_cfg.DATA_AUGMENTOR)
flag = True
return flag |