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train.py
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executable file
·148 lines (129 loc) · 6.4 KB
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#!/usr/bin/python3 #coding=utf-8
import os
import sys
import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
try:
from tensorboardX import SummaryWriter
BOARD_FLAG = True
except:
BOARD_FLAG = False
from lib import dataset
from lib.dataset import train_collate_fn
from network import Segment
import logging as logger
from lib.data_prefetcher import DataPrefetcher
import argparse
from lib.utils import load_model
DATA_PATH = "data/RGBD_sal/train"
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def train(conf, Dataset, Network):
# dataset
cfg = Dataset.Config(datapath=DATA_PATH, savepath=conf.savepath, mode='train', batch=conf.bz_size, lr=conf.lr, momen=0.9, decay=conf.decay, epoch=conf.epochs, train_scales=[224, 256, 320])
data = Dataset.RGBDData(cfg)
loader = DataLoader(data, batch_size=cfg.batch, shuffle=True, num_workers=8, drop_last=True, collate_fn=train_collate_fn)
# network
net = Network(backbone='resnet50', cfg=cfg, norm_layer=nn.SyncBatchNorm)
#
if os.path.exists(conf.model_pt):
msg = "Loading pretrained model_pt:%s"%conf.model_pt
print(msg)
logger.info(msg)
net.load_state_dict(torch.load(conf.model_pt), strict=True)
net.train()
net = nn.DataParallel(net)
net.cuda()
## parameter
rgb_base, rgbd_base, d_base, head = [], [], [], []
fc_params = []
for name, param in net.named_parameters():
if 'bkbone_rgbd' in name:
rgbd_base.append(param)
elif 'bkbone_rgb' in name:
rgb_base.append(param)
elif 'bkbone_d' in name:
d_base.append(param)
elif 'fc' in name:
print("add fc_params:", name)
fc_params.append(param)
else:
head.append(param)
assert len(rgbd_base) == 0
optimizer = torch.optim.SGD([{'params':rgb_base}, {'params':d_base},{'params':fc_params}, {'params':head}], lr=cfg.lr, momentum=cfg.momen, weight_decay=cfg.decay)# nesterov=True)
if BOARD_FLAG:
sw = SummaryWriter(cfg.savepath)
global_step = 0
for epoch in range(cfg.epoch):
optimizer.param_groups[0]['lr'] = (1-abs((epoch+1)/(cfg.epoch+1)*2-1))*cfg.lr*0.1
optimizer.param_groups[1]['lr'] = (1-abs((epoch+1)/(cfg.epoch+1)*2-1))*cfg.lr*0.1
optimizer.param_groups[2]['lr'] = (1-abs((epoch+1)/(cfg.epoch+1)*2-1))*cfg.lr
optimizer.param_groups[3]['lr'] = (1-abs((epoch+1)/(cfg.epoch+1)*2-1))*cfg.lr
step = -1
prefetcher = DataPrefetcher(loader, cnt=3)
image, depth, mask, gate_gt = prefetcher.next()
while image is not None:
out, out2_1, out3_1, out4_1, out5_1, \
out2_2, out3_2, out4_2, out5_2, gate = net.forward(image, depth)
# dominant loss
dom_loss = F.binary_cross_entropy_with_logits(out, mask)
# aux. loss
loss2_1 = F.binary_cross_entropy_with_logits(out2_1, mask)
loss3_1 = F.binary_cross_entropy_with_logits(out3_1, mask)
loss4_1 = F.binary_cross_entropy_with_logits(out4_1, mask)
loss5_1 = F.binary_cross_entropy_with_logits(out5_1, mask)
loss2_2 = F.binary_cross_entropy_with_logits(out2_2, mask)
loss3_2 = F.binary_cross_entropy_with_logits(out3_2, mask)
loss4_2 = F.binary_cross_entropy_with_logits(out4_2, mask)
loss5_2 = F.binary_cross_entropy_with_logits(out5_2, mask)
# regression
reg_loss = F.smooth_l1_loss(gate, gate_gt) * 2
loss = dom_loss + reg_loss + 0.8*(loss2_1*1 + loss3_1*0.8 + loss4_1*0.6 + loss5_1*0.4) + 0.8*(loss2_2*1 + loss3_2*0.8 + loss4_2*0.6 + loss5_2*0.4)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# log
global_step += 1
step += 1
if BOARD_FLAG:
sw.add_scalar('lr' , optimizer.param_groups[0]['lr'], global_step=global_step)
sw.add_scalars('loss', {'dom_loss':dom_loss.item(), 'loss2_1':loss2_1.item(), 'loss3_1':loss3_1.item(), 'loss4_1':loss4_1.item(), 'loss5_1':loss5_1.item(),
'reg_loss':reg_loss.item(), 'loss':loss.item()}, global_step=global_step)
if step%10 == 0:
msg = '%s | step:%d/%d/%d | lr=%.6f | loss=%.6f | dom_loss=%.6f | loss2=%.6f | loss3=%.6f | loss4=%.6f | loss5=%.6f | loss2_2=%.6f | loss3_2=%.6f | loss4_2=%.6f | loss5_2=%.6f | reg_loss=%.6f'%(datetime.datetime.now(), global_step, epoch+1, cfg.epoch, optimizer.param_groups[0]['lr'], loss.item(), dom_loss.item(), loss2_1.item(), loss3_1.item(), loss4_1.item(), loss5_1.item(), loss2_2.item(), loss3_2.item(), loss4_2.item(), loss5_2.item(), reg_loss.item())
print(msg)
logger.info(msg)
image, depth, mask, gate_gt = prefetcher.next()
if (epoch+1) in [cfg.epoch, cfg.epoch-1, cfg.epoch-2,\
cfg.epoch-3, cfg.epoch-4]: # or dom_loss.item() <= 0.025:
#logger.info("saving model-%s ..., loss:%s"%(epoch+1, dom_loss.item()))
torch.save(net.module.state_dict(), cfg.savepath+'/model-'+str(epoch+1))
if __name__=='__main__':
conf = argparse.ArgumentParser(description="train model")
conf.add_argument("--tag", type=str)
conf.add_argument("--savepath", type=str, help="where to save models?")
conf.add_argument("--lr", type=float, default=0.05)
conf.add_argument("--bz_size", type=int, default=32)
conf.add_argument("--epochs", type=int, default=30)
conf.add_argument("--decay", type=float, default=1e-4)
conf.add_argument("--seed", type=int, default=1997)
conf.add_argument("--gpu", type=str, default="0,1,2,3")
conf.add_argument("--model_pt", type=str, default="None")
args = conf.parse_args()
logger.basicConfig(level=logger.INFO, format='%(levelname)s %(asctime)s %(filename)s: %(lineno)d] %(message)s', datefmt='%Y-%m-%d %H:%M:%S', \
filename="train_%s.log"%(args.tag), filemode="w")
logger.info("Configuration:{}".format(args))
logger.info("SEED:%s, gpu:%s"%(args.seed, args.gpu))
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
setup_seed(args.seed)
if not os.path.exists(args.savepath):
os.makedirs(args.savepath)
train(args, dataset, Segment)