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train.py
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import os
import shutil
import time
import datetime
from typing import Dict
import numpy as np
import torch
from torch.backends import cudnn
from model.model import parsingNet
from data.dataloader import get_train_loader
from utils.dist_utils import dist_print, dist_tqdm, is_main_process, DistSummaryWriter
from utils.factory import MultiStepLR, get_metric_dict, get_loss_dict, get_optimizer, get_scheduler
from utils.metrics import MultiLabelAcc, AccTopk, Metric_mIoU, update_metrics, reset_metrics
from utils.common import merge_config, save_model, cp_projects
from utils.common import get_work_dir, get_logger
cudnn.benchmark = True
def main():
args, cfg = merge_config()
work_dir = get_work_dir(cfg)
if args.config:
os.makedirs(work_dir, exist_ok=True)
shutil.copy(args.config, os.path.join(work_dir, os.path.split(args.config)[1])) # save config file
distributed = False
if 'WORLD_SIZE' in os.environ:
distributed = int(os.environ['WORLD_SIZE']) > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
dist_print(datetime.datetime.now().strftime('[%Y/%m/%d %H:%M:%S]') + ' start training...')
dist_print(cfg)
assert cfg.backbone in ['18', '34', '50', '101', '152', '50next', '101next', '50wide', '101wide']
train_loader, cls_num_per_lane = get_train_loader(
cfg.batch_size, cfg.data_root, cfg.griding_num, cfg.dataset, cfg.use_aux, distributed, cfg.num_lanes
)
net = parsingNet(
pretrained=True,
backbone=cfg.backbone,
cls_dim=(cfg.griding_num + 1, cls_num_per_lane, cfg.num_lanes),
use_aux=cfg.use_aux,
).cuda()
if distributed:
net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.local_rank])
optimizer = get_optimizer(net, cfg)
if cfg.finetune is not None:
dist_print('finetune from ', cfg.finetune)
state_all = torch.load(cfg.finetune)['model']
state_clip = {} # only use backbone parameters
for k, v in state_all.items():
if 'model' in k:
state_clip[k] = v
net.load_state_dict(state_clip, strict=False)
if cfg.resume is not None:
dist_print('==> Resume model from ' + cfg.resume)
resume_dict = torch.load(cfg.resume, map_location='cpu')
net.load_state_dict(resume_dict['model'])
if 'optimizer' in resume_dict.keys():
optimizer.load_state_dict(resume_dict['optimizer'])
resume_epoch = int(os.path.split(cfg.resume)[1][2 : 5]) + 1
else:
resume_epoch = 0
scheduler = get_scheduler(optimizer, cfg, len(train_loader))
dist_print(len(train_loader))
metric_dict = get_metric_dict(cfg)
loss_dict = get_loss_dict(cfg)
logger = get_logger(work_dir, cfg)
cp_projects(args.auto_backup, work_dir)
for epoch in range(resume_epoch, cfg.epoch):
net.train()
progress_bar = dist_tqdm(train_loader)
t_data_0 = time.time()
for b_idx, data_label in enumerate(progress_bar):
t_data_1 = time.time()
reset_metrics(metric_dict)
global_step = epoch * len(train_loader) + b_idx
t_net_0 = time.time()
results = inference(net, data_label, cfg.use_aux)
loss = calc_loss(loss_dict, results, logger, global_step)
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step(global_step)
t_net_1 = time.time()
results = resolve_val_data(results, cfg.use_aux)
update_metrics(metric_dict, results)
if global_step % 20 == 0:
for me_name, me_op in zip(metric_dict['name'], metric_dict['op']):
logger.add_scalar('metric/' + me_name, me_op.get(), global_step=global_step)
logger.add_scalar('meta/lr', optimizer.param_groups[0]['lr'], global_step=global_step)
if hasattr(progress_bar, 'set_postfix'):
kwargs = {me_name: me_op.get() for me_name, me_op in zip(metric_dict['name'], metric_dict['op'])}
if "iou" in kwargs.keys():
iou_item = f"{kwargs['iou']:.3f}"
else:
iou_item = "'no_iou'"
log_dict = {
"epoch": f"{epoch}/{cfg.epoch}",
"lr": f"{optimizer.param_groups[0]['lr']:.6f}",
"top1": f"{kwargs['top1']:.3f}",
"iou": iou_item,
"loss": f"{loss:.3f}",
# "t_data": f"{t_data_1 - t_data_0:.3f}",
# "t_net": f"{t_net_1 - t_net_0:.3f}",
}
progress_bar.set_description(" ".join([f"\033[01;32m[{k}]\033[0m{v}" for k, v in log_dict.items()]))
t_data_0 = time.time()
if epoch % 10 == 0:
save_model(net, optimizer, epoch, work_dir, distributed)
logger.close()
def inference(net, data_label, use_aux):
if use_aux:
img, cls_label, seg_label = data_label
img, cls_label, seg_label = img.cuda(), cls_label.long().cuda(), seg_label.long().cuda()
cls_out, seg_out = net(img)
return {'cls_out': cls_out, 'cls_label': cls_label, 'seg_out': seg_out, 'seg_label': seg_label}
else:
img, cls_label = data_label
img, cls_label = img.cuda(), cls_label.long().cuda()
cls_out = net(img)
return {'cls_out': cls_out, 'cls_label': cls_label}
def resolve_val_data(results, use_aux):
results['cls_out'] = torch.argmax(results['cls_out'], dim=1)
if use_aux:
results['seg_out'] = torch.argmax(results['seg_out'], dim=1)
return results
def calc_loss(loss_dict, results, logger, global_step):
loss = 0
for i in range(len(loss_dict['name'])):
data_src = loss_dict['data_src'][i]
datas = [results[src] for src in data_src]
loss_cur = loss_dict['op'][i](*datas)
if global_step % 20 == 0:
logger.add_scalar('loss/' + loss_dict['name'][i], loss_cur, global_step)
loss += loss_cur * loss_dict['weight'][i]
return loss
if __name__ == "__main__":
main()