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train.py
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train.py
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import os.path as osp
import os
# os.environ['CUDA_VISIBLE_DEVICES'] = '3'
# os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
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 utils.config_utils import get_config_by_file
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 utils.losses import BCEDiceLoss
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()
config = get_config_by_file(args.config_file)
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, config)
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
if config.num_classes > 2:
criterion = nn.CrossEntropyLoss(reduction='mean', ignore_index=config.background)
else:
criterion = BCEDiceLoss()
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']
# gts = torch.unsqueeze(gts, axis=1)
# print(gts.dtype)
# gts = gts.to(torch.float)
# print(gts.dtype)
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)