def train_CRN_Net(args):
cfg = Params(os.path.join("../config", args.config))
# set up params
cfg.ctx = [int(i) for i in args.gpu.split(',')]
if len(cfg.ctx) > 1:
Exception('Only for 1-GPU mode')
cfg_attr = vars(cfg)
cfg_attr.update(vars(args))
# set up data loader
trainIter = dataIter(cfg)
# set up model
if cfg.backbone == 'CRN_Res101':
model = CRN(mtype=101, num_classes=1)
if cfg.backbone == 'CRN_Res50':
model = CRN(mtype=50, num_classes=1)
if args.resume:
mdl_dir = args.model_dir
pre_pfx = args.pretrained_prefix
pre_epc = args.pretrained_epoch
net_pfx = cfg.network
saved_state_dict = torch.load(
os.path.join(mdl_dir, pre_pfx, net_pfx + '_' + pre_epc + '.pth'))
model.load_state_dict(saved_state_dict)
if cfg.use_global_stats == True:
model.eval() #
if len(cfg.ctx) > 0:
model.cuda(cfg.ctx[0])
# set up optimizer
if cfg.optimizer == 'SGD':
optimizer = optim.SGD(model.parameters(),
lr=cfg.learning_rate,
momentum=cfg.momentum,
weight_decay=cfg.wd) #
elif cfg.optimizer == 'Adam':
optimizer = optim.Adam(model.parameters(),
lr=cfg.learning_rate,
weight_decay=cfg.wd)
else:
Exception('SGD or Adam')
optimizer.zero_grad()
# set up model path
model_path = os.path.join(args.model_dir, cfg.prefix)
if not os.path.isdir(model_path):
os.mkdir(model_path)
model_full_path = os.path.join(model_path,
datetime.now().strftime('%Y_%m_%d_%H_%M'))
if not os.path.isdir(model_full_path):
os.mkdir(model_full_path)
# set up log
util.save_log(cfg.prefix, model_full_path)
logging.info(
'---------------------------TIME-------------------------------')
logging.info('-------------------{}------------------------'.format(
datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
for k, v in sorted(cfg_attr.items(), key=lambda x: x[0]):
logging.info("%s : %s", k, v)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# training phase
for epoch in range(cfg.begin_epoch, cfg.end_epoch):
trainIter.reset()
totalLoss = 0.0
tic = time.time()
for iter in tqdm(range(trainIter.iter_cnt)):
img, msk_16, msk_32, gt0, gt1, gt2, gt3, gt4, gt5 = trainIter.next(
)
out = model([img, msk_32])
loss0 = Loss_calc(out[0], gt0)
loss1 = Loss_calc(out[1], gt1)
loss2 = Loss_calc(out[2], gt2)
loss3 = Loss_calc(out[3], gt3)
loss4 = Loss_calc(out[4], gt4)
loss5 = Loss_calc(out[5], gt5)
loss = loss0 + loss1 + loss2 + loss3 + loss4 + loss5
loss.backward()
if iter % cfg.updateIter == 0:
optimizer.step()
optimizer.zero_grad()
totalLoss += loss.data.cpu().numpy() / trainIter.iter_cnt
logger.info('Epoch[%d] Train-Loss=%.5f', epoch, totalLoss)
toc = time.time()
logger.info('Epoch[%d] Time cost=%.3f', epoch, (toc - tic))
if epoch % cfg.frequence == 0:
print 'taking snapshot: ' + os.path.join(cfg.network + '_' +
str(epoch) + '.pth')
torch.save(
model.cpu().state_dict(),
os.path.join(model_full_path,
cfg.network + '-' + str(epoch) + '.pth'))
model.cuda(cfg.ctx[0])
def train_Tube_Net(args):
cfg = Params(os.path.join("../config",args.config))
# set up params
cfg.ctx = [int(i) for i in args.gpu.split(',')]
if len(cfg.ctx)>1:
Exception('Only for 1-GPU mode')
cfg_attr = vars(cfg)
cfg_attr.update(vars(args))
# set up model path
model_path = os.path.join(args.model_dir, cfg.prefix)
if not os.path.isdir(model_path):
os.mkdir(model_path)
model_full_path = os.path.join(
model_path, datetime.now().strftime('%Y_%m_%d_%H_%M'))
if not os.path.isdir(model_full_path):
os.mkdir(model_full_path)
# set up log
util.save_log(cfg.prefix, model_full_path)
logging.info(
'---------------------------TIME-------------------------------')
logging.info('-------------------{}------------------------'.format(
datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
for k, v in sorted(cfg_attr.items(), key=lambda x: x[0]):
logging.info("%s : %s", k, v)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
name_list, f1_img, f1_gt, img_list, label_list = read_vos_test_list(cfg)
mIoU = 0.0
MAE = 0.0
seq_num = len(name_list)
for seq_i in range(seq_num):
seq = name_list[seq_i]
f1_i = f1_img[seq_i]
f1_g = f1_gt[seq_i]
imgs = img_list[seq_i]
labels = label_list[seq_i]
# set up model
if cfg.backbone == 'CRN_Res101':
model = CRN(mtype=101,num_classes=1)
if cfg.backbone == 'CRN_Res50':
model = CRN(mtype=50,num_classes=1)
if args.resume==1:
print('load model:'+args.trained_path)
logging.info('load model:'+args.trained_path)
saved_state_dict = torch.load(args.trained_path)
model.load_state_dict(saved_state_dict)
if cfg.use_global_stats == True:
model.eval() # use_global_stats = True
if len(cfg.ctx)>0:
model.cuda(cfg.ctx[0])
# set up optimizer
if cfg.optimizer == 'SGD':
optimizer = optim.SGD(model.parameters(),lr = cfg.learning_rate,
momentum = cfg.momentum, weight_decay = cfg.wd)#
elif cfg.optimizer == 'Adam':
optimizer = optim.Adam(model.parameters(),lr = cfg.learning_rate, weight_decay = cfg.wd)
else:
Exception('SGD or Adam')
optimizer.zero_grad()
totalLoss = 0.0
tic = time.time()
logger.info('Finetuning on the set- %s', seq)
for epoch in tqdm(range(cfg.finetune_epoch)):
img,msk_16,msk_32,gt0,gt1,gt2,gt3,gt4,gt5= get_training_batch(cfg,f1_i,f1_g)
out = model([img,msk_32])
loss0 = Loss_calc(out[0],gt0)
loss1 = Loss_calc(out[1],gt1)
loss2 = Loss_calc(out[2],gt2)
loss3 = Loss_calc(out[3],gt3)
loss4 = Loss_calc(out[4],gt4)
loss5 = Loss_calc(out[5],gt5)
loss = loss0 + loss1 + loss2 + loss3 + loss4 + loss5
loss.backward()
optimizer.step()
optimizer.zero_grad()
totalLoss += loss.data.cpu().numpy()/cfg.finetune_epoch
model.eval()
torch.save(model.cpu().state_dict(),os.path.join(model_full_path,seq+'_iter'+str(cfg.finetune_epoch)+'.pth'))
model.cuda(cfg.ctx[0])
toc = time.time()
logger.info('Train-Loss=%.5f, Time cost=%.3f', totalLoss,(toc - tic))
iouu = 0.0
mae = 0.0
img_ = imgs[0]
label_ = labels[0]
img_test,lbl_test,mask_test=get_testing_batch(cfg,img_,label_)
mask__ = lbl_test[0,0].cpu().data.numpy()
for test_ in range(len(imgs)):
model.eval()
torch.cuda.empty_cache()
img_ = imgs[test_]
label_ = labels[test_]
img_test,lbl_test,mask_test=get_testing_batch(cfg,img_,label_)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(8,8))
mask__ = cv2.dilate(mask__,kernel)
mask_temp = mask__>0
msk_atn0 = cv2.resize(mask_temp.astype(np.float),(256,256) , interpolation = cv2.INTER_NEAREST)
msk_atn0 = Variable(torch.from_numpy(msk_atn0).float().view(1,1,256,256)).cuda(cfg.ctx[0])
msk_atn1 = cv2.resize(mask_temp.astype(np.float),(128,128) , interpolation = cv2.INTER_NEAREST)
msk_atn1 = Variable(torch.from_numpy(msk_atn1).float().view(1,1,128,128)).cuda(cfg.ctx[0])
msk_atn2 = cv2.resize(mask_temp.astype(np.float),(64,64) , interpolation = cv2.INTER_NEAREST)
msk_atn2 = Variable(torch.from_numpy(msk_atn2).float().view(1,1,64,64)).cuda(cfg.ctx[0])
msk_atn3 = cv2.resize(mask_temp.astype(np.float),(32,32) , interpolation = cv2.INTER_NEAREST)
msk_atn3 = Variable(torch.from_numpy(msk_atn3).float().view(1,1,32,32)).cuda(cfg.ctx[0])
msk_atn4 = cv2.resize(mask_temp.astype(np.float),(16,16) , interpolation = cv2.INTER_NEAREST)
msk_atn4 = Variable(torch.from_numpy(msk_atn4).float().view(1,1,16,16)).cuda(cfg.ctx[0])
mask_ = cv2.resize(mask__.astype(np.float),(16,16) )
mask_t = Variable(torch.from_numpy(mask_).float().view(1,16,16))
out = model([img_test,mask_t.cuda(cfg.ctx[0])])
mask_out = (out[0][0,0].cpu().data.numpy()>0.5).astype(np.float)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(64, 64))
mask__ = cv2.dilate(mask__,kernel)
mask__ = mask_out*mask__
lbl_test = (lbl_test).cpu().data.numpy()
#cv2.imshow("mask__",mask__)
#cv2.waitKey (50)
iouu += compute_iou_for_binary_segmentation(mask__>0.5,lbl_test[:,:]>0.5)
mae += (np.abs(mask__-lbl_test[:,:])).sum()
mIoU += iouu/len(imgs)
MAE += mae/len(imgs)/(cfg.frame_num*cfg.img_size/8*cfg.img_size/8)
logger.info('Testing: mIoU-%.5f, MAE-%.5f', iouu/len(imgs), mae/len(imgs)/(cfg.frame_num*cfg.img_size/8*cfg.img_size/8))
logger.info('---------------------------------')
logger.info('####################################')
logger.info('Total Testing: mIoU-%.5f, MAE-%.5f', mIoU/seq_num, MAE/seq_num)
logger.info('####################################')