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train_voc.py
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import argparse
from itertools import cycle
import logging
import os
import pprint
import torch
import numpy as np
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.optim import SGD
from torch.utils.data import DataLoader
import yaml
from dataset.sass import *
from model.semseg.deeplabv3plus import DeepLabV3Plus
from util.ohem import ProbOhemCrossEntropy2d
from util.utils import count_params, AverageMeter, intersectionAndUnion, init_log, evaluate
from util.dist_helper import setup_distributed
os.environ['CUDA_VISIBLE_DEVICES'] = "0, 1, 2"
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '28890'
# sh tools/train_voc.sh 3 28890
parser = argparse.ArgumentParser(description='Sparsely-annotated Semantic Segmentation')
parser.add_argument('--config', type=str, required=True)
parser.add_argument('--save-path', type=str, required=True)
parser.add_argument('--local_rank', default=0, type=int)
parser.add_argument('--port', default=None, type=int)
def main():
args = parser.parse_args()
cfg = yaml.load(open(args.config, "r"), Loader=yaml.Loader)
logger = init_log('global', logging.INFO)
logger.propagate = 0
rank, word_size = setup_distributed(port=args.port)
if rank == 0:
logger.info('{}\n'.format(pprint.pformat(cfg)))
if rank == 0:
os.makedirs(args.save_path, exist_ok=True)
cudnn.enabled = True
cudnn.benchmark = True
model = DeepLabV3Plus(cfg, aux=cfg['aux'])
if rank == 0:
logger.info('Total params: {:.1f}M\n'.format(count_params(model)))
optimizer = SGD([{'params': model.backbone.parameters(), 'lr': cfg['lr']},
{'params': [param for name, param in model.named_parameters() if 'backbone' not in name],
'lr': cfg['lr'] * cfg['lr_multi']}], lr=cfg['lr'], momentum=0.9, weight_decay=1e-4)
local_rank = int(os.environ["LOCAL_RANK"])
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model.cuda(local_rank)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank],
output_device=local_rank, find_unused_parameters=False)
ohem = False if cfg['criterion']['name'] == 'CELoss' else True
use_weight = True if cfg['dataset'] == 'cityscapes' else False
if cfg['dataset'] == 'pascal':
trainset = VocDataset(cfg['dataset'], cfg['data_root'], cfg['mode'],
cfg['crop_size'], cfg['aug'])
valset = VocDataset(cfg['dataset'], cfg['data_root'], 'val', None)
else:
trainset = CityDataset(cfg['dataset'], cfg['data_root'], cfg['mode'],
cfg['crop_size'], cfg['aug'])
valset = CityDataset(cfg['dataset'], cfg['data_root'], 'val', None)
trainsampler = torch.utils.data.distributed.DistributedSampler(trainset)
trainloader = DataLoader(trainset, batch_size=cfg['batch_size'],
pin_memory=True, num_workers=4, drop_last=True, sampler=trainsampler)
valsampler = torch.utils.data.distributed.DistributedSampler(valset)
valloader = DataLoader(valset, batch_size=1, pin_memory=True, num_workers=2,
drop_last=False, sampler=valsampler)
iters = 0
total_iters = len(trainloader) * cfg['epochs']
previous_best = 0.0
for epoch in range(cfg['epochs']):
if rank == 0:
logger.info('===========> Epoch: {:}, LR: {:.4f}, Previous best: {:.2f}'.format(
epoch, optimizer.param_groups[0]['lr'], previous_best))
model.train()
loss_m = AverageMeter()
seg_m = AverageMeter()
gmm_m = AverageMeter()
trainsampler.set_epoch(epoch)
for i, (img, mask, cls_label, id) in enumerate(trainloader):
img, mask, cls_label = img.cuda(), mask.cuda(), cls_label.cuda()
feat, pred = model(img)
seg_loss = loss_calc(pred, mask,
ignore_index=cfg['nclass'], multi=False,
class_weight=use_weight, ohem=ohem)
cls_loss = get_cls_loss(pred, cls_label, mask)
# Gaussian
cur_cls_label = build_cur_cls_label(mask, cfg['nclass'])
pred_cl = clean_mask(pred, cls_label, True)
vecs, proto_loss = cal_protypes(feat, mask, cfg['nclass'])
res = GMM(feat, vecs, pred_cl, mask, cur_cls_label)
gmm_loss = cal_gmm_loss(pred.softmax(1), res, cur_cls_label, mask) + proto_loss + cls_loss
# total loss
loss = seg_loss + gmm_loss
# for ablation
# loss = seg_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_m.update(loss.item(), img.size()[0])
seg_m.update(seg_loss.item(), img.size()[0])
gmm_m.update(gmm_loss.item(), img.size()[0])
iters += 1
lr = cfg['lr'] * (1 - iters / total_iters) ** 0.9
optimizer.param_groups[0]["lr"] = lr
optimizer.param_groups[1]["lr"] = lr * cfg['lr_multi']
if (i % (max(2, len(trainloader) // 8)) == 0) and (rank == 0):
logger.info('Iters:{:}, loss:{:.3f}, seg_loss:{:.3f}, '
'gmm_loss:{:.3f}'.format
(i, loss_m.avg, seg_m.avg, gmm_m.avg))
if cfg['dataset'] == 'cityscapes':
eval_mode = 'center_crop' if epoch < cfg['epochs'] - 20 else 'sliding_window'
else:
eval_mode = 'original'
mIOU, iou_class = evaluate(model, valloader, eval_mode, cfg)
if rank == 0:
logger.info('***** Evaluation {} ***** >>>> meanIOU: {:.2f}\n'.format(eval_mode, mIOU))
if mIOU > previous_best and rank == 0:
if previous_best != 0:
os.remove(os.path.join(args.save_path, '%s_%.2f.pth' % (cfg['backbone'], previous_best)))
previous_best = mIOU
torch.save(model.module.state_dict(),
os.path.join(args.save_path, '%s_%.2f.pth' % (cfg['backbone'], mIOU)))
if __name__ == '__main__':
main()