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train.py
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train.py
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import os.path as osp
import os
import sys
import time
import argparse
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torch.nn.parallel import DistributedDataParallel
from config import config
from dataloader.dataloader import get_train_loader
from models.builder import EncoderDecoder as segmodel
from dataloader.RGBXDataset import RGBXDataset
from utils.init_func import init_weight, group_weight
from utils.lr_policy import WarmUpPolyLR
from engine.engine import Engine
from engine.logger import get_logger
from utils.pyt_utils import all_reduce_tensor
from tensorboardX import SummaryWriter
parser = argparse.ArgumentParser()
logger = get_logger()
os.environ['MASTER_PORT'] = '169710'
with Engine(custom_parser=parser) as engine:
args = parser.parse_args()
cudnn.benchmark = True
seed = config.seed
if engine.distributed:
seed = engine.local_rank
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
# data loader
train_loader, train_sampler = get_train_loader(engine, RGBXDataset)
if (engine.distributed and (engine.local_rank == 0)) or (not engine.distributed):
tb_dir = config.tb_dir + '/{}'.format(time.strftime("%b%d_%d-%H-%M", time.localtime()))
generate_tb_dir = config.tb_dir + '/tb'
tb = SummaryWriter(log_dir=tb_dir)
engine.link_tb(tb_dir, generate_tb_dir)
# config network and criterion
criterion = nn.CrossEntropyLoss(reduction='mean', ignore_index=config.background)
if engine.distributed:
BatchNorm2d = nn.SyncBatchNorm
else:
BatchNorm2d = nn.BatchNorm2d
model=segmodel(cfg=config, criterion=criterion, norm_layer=BatchNorm2d)
# group weight and config optimizer
base_lr = config.lr
if engine.distributed:
base_lr = config.lr
params_list = []
params_list = group_weight(params_list, model, BatchNorm2d, base_lr)
if config.optimizer == 'AdamW':
optimizer = torch.optim.AdamW(params_list, lr=base_lr, betas=(0.9, 0.999), weight_decay=config.weight_decay)
elif config.optimizer == 'SGDM':
optimizer = torch.optim.SGD(params_list, lr=base_lr, momentum=config.momentum, weight_decay=config.weight_decay)
else:
raise NotImplementedError
# config lr policy
total_iteration = config.nepochs * config.niters_per_epoch
lr_policy = WarmUpPolyLR(base_lr, config.lr_power, total_iteration, config.niters_per_epoch * config.warm_up_epoch)
if engine.distributed:
logger.info('.............distributed training.............')
if torch.cuda.is_available():
model.cuda()
model = DistributedDataParallel(model, device_ids=[engine.local_rank],
output_device=engine.local_rank, find_unused_parameters=False)
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
engine.register_state(dataloader=train_loader, model=model,
optimizer=optimizer)
if engine.continue_state_object:
engine.restore_checkpoint()
optimizer.zero_grad()
model.train()
logger.info('begin trainning:')
for epoch in range(engine.state.epoch, config.nepochs+1):
if engine.distributed:
train_sampler.set_epoch(epoch)
bar_format = '{desc}[{elapsed}<{remaining},{rate_fmt}]'
pbar = tqdm(range(config.niters_per_epoch), file=sys.stdout,
bar_format=bar_format)
dataloader = iter(train_loader)
sum_loss = 0
for idx in pbar:
engine.update_iteration(epoch, idx)
minibatch = dataloader.next()
imgs = minibatch['data']
gts = minibatch['label']
modal_xs = minibatch['modal_x']
imgs = imgs.cuda(non_blocking=True)
gts = gts.cuda(non_blocking=True)
modal_xs = modal_xs.cuda(non_blocking=True)
aux_rate = 0.2
loss = model(imgs, modal_xs, gts)
# reduce the whole loss over multi-gpu
if engine.distributed:
reduce_loss = all_reduce_tensor(loss, world_size=engine.world_size)
optimizer.zero_grad()
loss.backward()
optimizer.step()
current_idx = (epoch- 1) * config.niters_per_epoch + idx
lr = lr_policy.get_lr(current_idx)
for i in range(len(optimizer.param_groups)):
optimizer.param_groups[i]['lr'] = lr
if engine.distributed:
sum_loss += reduce_loss.item()
print_str = 'Epoch {}/{}'.format(epoch, config.nepochs) \
+ ' Iter {}/{}:'.format(idx + 1, config.niters_per_epoch) \
+ ' lr=%.4e' % lr \
+ ' loss=%.4f total_loss=%.4f' % (reduce_loss.item(), (sum_loss / (idx + 1)))
else:
sum_loss += loss
print_str = 'Epoch {}/{}'.format(epoch, config.nepochs) \
+ ' Iter {}/{}:'.format(idx + 1, config.niters_per_epoch) \
+ ' lr=%.4e' % lr \
+ ' loss=%.4f total_loss=%.4f' % (loss, (sum_loss / (idx + 1)))
del loss
pbar.set_description(print_str, refresh=False)
if (engine.distributed and (engine.local_rank == 0)) or (not engine.distributed):
tb.add_scalar('train_loss', sum_loss / len(pbar), epoch)
if (epoch >= config.checkpoint_start_epoch) and (epoch % config.checkpoint_step == 0) or (epoch == config.nepochs):
if engine.distributed and (engine.local_rank == 0):
engine.save_and_link_checkpoint(config.checkpoint_dir,
config.log_dir,
config.log_dir_link)
elif not engine.distributed:
engine.save_and_link_checkpoint(config.checkpoint_dir,
config.log_dir,
config.log_dir_link)