def train(self,damage_initial_previous_frame_mask=True,lossfunc='cross_entropy',model_resume=False):
###################
self.model.train()
running_loss = AverageMeter()
#optimizer = optim.SGD([{'params':self.model.feature_extracter.parameters()},{'params':self.model.semantic_embedding.parameters()},{'params':self.model.dynamic_seghead.parameters()}],lr=cfg.TRAIN_LR,momentum=cfg.TRAIN_MOMENTUM)
optimizer = optim.SGD(self.model.parameters(),lr=cfg.TRAIN_LR,momentum=cfg.TRAIN_MOMENTUM,weight_decay=cfg.TRAIN_WEIGHT_DECAY)
#scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=cfg.TRAIN_LR_STEPSIZE,gamma=cfg.TRAIN_LR_GAMMA)
###################
composed_transforms = transforms.Compose([tr.RandomHorizontalFlip(cfg.DATA_RANDOMFLIP),
tr.RandomScale(),
tr.RandomCrop((cfg.DATA_RANDOMCROP,cfg.DATA_RANDOMCROP)),
tr.Resize(cfg.DATA_RESCALE),
tr.ToTensor()])
composed_transforms_ytb = transforms.Compose([tr.RandomHorizontalFlip(cfg.DATA_RANDOMFLIP),
tr.RandomScale([0.5,1,1.25]),
tr.RandomCrop((800,800)),
tr.Resize(cfg.DATA_RESCALE),
tr.ToTensor()])
print('dataset processing...')
# train_dataset = DAVIS2017_Train(root=cfg.DATA_ROOT, transform=composed_transforms)
train_dataset = DAVIS2017_VOS_Train(root=cfg.DATA_ROOT, transform=composed_transforms)
ytb_train_dataset = YTB_VOS_Train(root=cfg.YTB_DATAROOT,transform=composed_transforms_ytb)
# trainloader = DataLoader(train_dataset,batch_size=cfg.TRAIN_BATCH_SIZE,
# sampler = RandomIdentitySampler(train_dataset.sample_list),
# shuffle=False,num_workers=cfg.NUM_WORKER,pin_memory=True)
trainloader_davis = DataLoader(train_dataset,batch_size=cfg.TRAIN_BATCH_SIZE,
shuffle=True,num_workers=cfg.NUM_WORKER,pin_memory=True)
trainloader_ytb = DataLoader(ytb_train_dataset,batch_size=cfg.TRAIN_BATCH_SIZE,
shuffle=True,num_workers=cfg.NUM_WORKER,pin_memory=True)
#trainloader=[trainloader_ytb,trainloader_davis]
trainloader=[trainloader_ytb,trainloader_davis]
print('dataset processing finished.')
if lossfunc=='bce':
criterion = Added_BCEWithLogitsLoss(cfg.TRAIN_TOP_K_PERCENT_PIXELS,cfg.TRAIN_HARD_MINING_STEP)
elif lossfunc=='cross_entropy':
criterion = Added_CrossEntropyLoss(cfg.TRAIN_TOP_K_PERCENT_PIXELS,cfg.TRAIN_HARD_MINING_STEP)
else:
print('unsupported loss funciton. Please choose from [cross_entropy,bce]')
max_itr = cfg.TRAIN_TOTAL_STEPS
step=0
if model_resume:
saved_model_=os.path.join(self.save_res_dir,'save_step_60000.pth')
saved_model_ = torch.load(saved_model_)
self.model=self.load_network(self.model,saved_model_)
step=60000
print('resume from step {}'.format(step))
while step<cfg.TRAIN_TOTAL_STEPS:
for train_dataloader in trainloader:
# sample['meta']={'seq_name':seqname,'frame_num':frame_num,'obj_num':obj_num}
for ii, sample in enumerate(train_dataloader):
# print(ii)
now_lr=self._adjust_lr(optimizer,step,max_itr)
ref_imgs = sample['ref_img'] #batch_size * 3 * h * w
img1s = sample['img1']
img2s = sample['img2']
ref_scribble_labels = sample['ref_scribble_label'] #batch_size * 1 * h * w
label1s = sample['label1']
label2s = sample['label2']
seq_names = sample['meta']['seq_name']
obj_nums = sample['meta']['obj_num']
bs,_,h,w = img2s.size()
inputs = torch.cat((ref_imgs,img1s,img2s),0)
if damage_initial_previous_frame_mask:
try:
label1s = damage_masks(label1s)
except:
label1s = label1s
print('damage_error')
##########
if self.use_gpu:
inputs = inputs.cuda()
ref_scribble_labels=ref_scribble_labels.cuda()
label1s = label1s.cuda()
label2s = label2s.cuda()
##########
tmp_dic = self.model(inputs,ref_scribble_labels,label1s,seq_names=seq_names,gt_ids=obj_nums,k_nearest_neighbors=cfg.KNNS)
label_and_obj_dic={}
label_dic={}
for i, seq_ in enumerate(seq_names):
label_and_obj_dic[seq_]=(label2s[i],obj_nums[i])
for seq_ in tmp_dic.keys():
tmp_pred_logits = tmp_dic[seq_]
tmp_pred_logits = nn.functional.interpolate(tmp_pred_logits,size=(h,w),mode = 'bilinear',align_corners=True)
tmp_dic[seq_]=tmp_pred_logits
label_tmp,obj_num = label_and_obj_dic[seq_]
obj_ids = np.arange(1,obj_num+1)
obj_ids = torch.from_numpy(obj_ids)
obj_ids = obj_ids.int()
if torch.cuda.is_available():
obj_ids = obj_ids.cuda()
if lossfunc == 'bce':
label_tmp = label_tmp.permute(1,2,0)
label = (label_tmp.float()==obj_ids.float())
label = label.unsqueeze(-1).permute(3,2,0,1)
label_dic[seq_]=label.float()
elif lossfunc =='cross_entropy':
label_dic[seq_]=label_tmp.long()
loss = criterion(tmp_dic,label_dic,step)
loss =loss/bs
######################################
if loss.item()>10000:
print(tmp_dic)
for k,v in tmp_dic.items():
v = v.cpu()
v = v.detach().numpy()
np.save(k+'.npy',v)
l=label_dic[k]
l=l.cpu().detach().numpy()
np.save('lab'+k+'.npy',l)
#continue
exit()
##########################################
optimizer.zero_grad()
loss.backward()
optimizer.step()
#scheduler.step()
running_loss.update(loss.item(),bs)
if step%1==0:
#print(torch.cuda.memory_allocated())
#print(torch.cuda.max_memory_cached())
#torch.cuda.empty_cache()
#torch.cuda.reset_max_memory_allocated()
print('step:{},now_lr:{} ,loss:{:.4f}({:.4f})'.format(step,now_lr ,running_loss.val,running_loss.avg))
#print(tmp_dic)
#print(seq_names)
# print('step:{}'.format(step))
show_ref_img = ref_imgs.cpu().numpy()[0]
show_img1 = img1s.cpu().numpy()[0]
show_img2 = img2s.cpu().numpy()[0]
mean = np.array([[[0.485]], [[0.456]], [[0.406]]])
sigma = np.array([[[0.229]], [[0.224]], [[0.225]]])
show_ref_img = show_ref_img*sigma+mean
show_img1 = show_img1*sigma+mean
show_img2 = show_img2*sigma+mean
show_gt = label2s.cpu()[0]
show_gt = show_gt.squeeze(0).numpy()
show_gtf = label2colormap(show_gt).transpose((2,0,1))
##########
show_preds = tmp_dic[seq_names[0]].cpu()
show_preds=nn.functional.interpolate(show_preds,size=(h,w),mode = 'bilinear',align_corners=True)
show_preds = show_preds.squeeze(0)
if lossfunc=='bce':
show_preds = (torch.sigmoid(show_preds)>0.5)
show_preds_s = torch.zeros((h,w))
for i in range(show_preds.size(0)):
show_preds_s[show_preds[i]]=i+1
elif lossfunc=='cross_entropy':
show_preds_s = torch.argmax(show_preds,dim=0)
show_preds_s = show_preds_s.numpy()
show_preds_sf = label2colormap(show_preds_s).transpose((2,0,1))
pix_acc = np.sum(show_preds_s==show_gt)/(h*w)
tblogger.add_scalar('loss', running_loss.avg, step)
tblogger.add_scalar('pix_acc', pix_acc, step)
tblogger.add_scalar('now_lr', now_lr, step)
tblogger.add_image('Reference image', show_ref_img, step)
tblogger.add_image('Previous frame image', show_img1, step)
tblogger.add_image('Current frame image', show_img2, step)
tblogger.add_image('Groud Truth', show_gtf, step)
tblogger.add_image('Predict label', show_preds_sf, step)
###########TODO
if step%5000==0 and step!=0:
self.save_network(self.model,step)
step+=1
def train(self,damage_initial_previous_frame_mask=True,lossfunc='cross_entropy',model_resume=False,eval_total=False,init_prev=False):
###################
interactor = interactive_robot.InteractiveScribblesRobot()
self.model.train()
running_loss = AverageMeter()
optimizer = optim.SGD(self.model.inter_seghead.parameters(),lr=cfg.TRAIN_LR,momentum=cfg.TRAIN_MOMENTUM,weight_decay=cfg.TRAIN_WEIGHT_DECAY)
# optimizer = optim.SGD(self.model.inter_seghead.parameters(),lr=cfg.TRAIN_LR,momentum=cfg.TRAIN_MOMENTUM)
#scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=cfg.TRAIN_LR_STEPSIZE,gamma=cfg.TRAIN_LR_GAMMA)
###################
composed_transforms = transforms.Compose([tr.RandomHorizontalFlip(cfg.DATA_RANDOMFLIP),
tr.RandomScale(),
tr.RandomCrop((cfg.DATA_RANDOMCROP,cfg.DATA_RANDOMCROP),10),
tr.Resize(cfg.DATA_RESCALE),
tr.ToTensor()])
print('dataset processing...')
train_dataset = DAVIS2017_Train(root=cfg.DATA_ROOT, transform=composed_transforms)
train_list = train_dataset.seqs
# train_dataset = DAVIS2017_VOS_Train(root=cfg.DATA_ROOT, transform=composed_transforms)
# trainloader = DataLoader(train_dataset,batch_size=cfg.TRAIN_BATCH_SIZE,
# shuffle=True,num_workers=cfg.NUM_WORKER,pin_memory=True)
print('dataset processing finished.')
if lossfunc=='bce':
criterion = Added_BCEWithLogitsLoss(cfg.TRAIN_TOP_K_PERCENT_PIXELS,cfg.TRAIN_HARD_MINING_STEP)
elif lossfunc=='cross_entropy':
criterion = Added_CrossEntropyLoss(cfg.TRAIN_TOP_K_PERCENT_PIXELS,cfg.TRAIN_HARD_MINING_STEP)
else:
print('unsupported loss funciton. Please choose from [cross_entropy,bce]')
max_itr = cfg.TRAIN_TOTAL_STEPS
step=0
round_=3
epoch_per_round=30
if model_resume:
saved_model_=os.path.join(self.save_res_dir,'save_step_75000.pth')
saved_model_ = torch.load(saved_model_)
self.model=self.load_network(self.model,saved_model_)
step=75000
print('resume from step {}'.format(step))
while step<cfg.TRAIN_TOTAL_STEPS:
if step>100001:
break
for r in range(round_):
if r==0:
print('start new')
global_map_tmp_dic={}
train_dataset.transform=transforms.Compose([tr.RandomHorizontalFlip(cfg.DATA_RANDOMFLIP),
tr.RandomScale(),
tr.RandomCrop((cfg.DATA_RANDOMCROP,cfg.DATA_RANDOMCROP)),
tr.Resize(cfg.DATA_RESCALE),
tr.ToTensor()])
train_dataset.init_ref_frame_dic()
trainloader = DataLoader(train_dataset,batch_size=cfg.TRAIN_BATCH_SIZE,
sampler = RandomIdentitySampler(train_dataset.sample_list),
shuffle=False,num_workers=cfg.NUM_WORKER,pin_memory=True)
print('round:{} start'.format(r))
for epoch in range(epoch_per_round):
for ii, sample in enumerate(trainloader):
now_lr=self._adjust_lr(optimizer,step,max_itr)
ref_imgs = sample['ref_img'] #batch_size * 3 * h * w
#img1s = sample['img1']
#img2s = sample['img2']
ref_scribble_labels = sample['ref_scribble_label'] #batch_size * 1 * h * w
#label1s = sample['label1']
#label2s = sample['label2']
seq_names = sample['meta']['seq_name']
obj_nums = sample['meta']['obj_num']
#frame_nums = sample['meta']['frame_num']
ref_frame_nums = sample['meta']['ref_frame_num']
ref_frame_gts=sample['ref_frame_gt']
bs,_,h,w = ref_imgs.size()
# print(ref_imgs.size())
# if r==0:
# ref_scribble_labels=self.rough_ROI(ref_scribble_labels)
##########
if self.use_gpu:
inputs = ref_imgs.cuda()
ref_scribble_labels=ref_scribble_labels.cuda()
ref_frame_gts = ref_frame_gts.cuda()
#label1s = label1s.cuda()
#label2s = label2s.cuda()
#print(inputs.size())
##########
with torch.no_grad():
self.model.feature_extracter.eval()
self.model.semantic_embedding.eval()
ref_frame_embedding = self.model.extract_feature(inputs)
if r==0:
first_inter=True
tmp_dic = self.model.int_seghead(ref_frame_embedding=ref_frame_embedding,ref_scribble_label=ref_scribble_labels,
prev_round_label=None,normalize_nearest_neighbor_distances=True,global_map_tmp_dic={},
seq_names=seq_names,gt_ids=obj_nums,k_nearest_neighbors=cfg.KNNS,
frame_num=ref_frame_nums,first_inter=first_inter)
else:
first_inter=False
prev_round_label=sample['prev_round_label']
# print(prev_round_label.size())
#prev_round_label=prev_round_label_dic[seq_names[0]]
prev_round_label=prev_round_label.cuda()
tmp_dic = self.model.int_seghead(ref_frame_embedding=ref_frame_embedding,ref_scribble_label=ref_scribble_labels,
prev_round_label=prev_round_label,normalize_nearest_neighbor_distances=True,global_map_tmp_dic={},
seq_names=seq_names,gt_ids=obj_nums,k_nearest_neighbors=cfg.KNNS,
frame_num=ref_frame_nums,first_inter=first_inter)
label_and_obj_dic={}
label_dic={}
for i, seq_ in enumerate(seq_names):
label_and_obj_dic[seq_]=(ref_frame_gts[i],obj_nums[i])
for seq_ in tmp_dic.keys():
tmp_pred_logits = tmp_dic[seq_]
tmp_pred_logits = nn.functional.interpolate(tmp_pred_logits,size=(h,w),mode = 'bilinear',align_corners=True)
tmp_dic[seq_]=tmp_pred_logits
label_tmp,obj_num = label_and_obj_dic[seq_]
obj_ids = np.arange(0,obj_num+1)
obj_ids = torch.from_numpy(obj_ids)
obj_ids = obj_ids.int()
if torch.cuda.is_available():
obj_ids = obj_ids.cuda()
if lossfunc == 'bce':
label_tmp = label_tmp.permute(1,2,0)
label = (label_tmp.float()==obj_ids.float())
label = label.unsqueeze(-1).permute(3,2,0,1)
label_dic[seq_]=label.float()
elif lossfunc =='cross_entropy':
label_dic[seq_]=label_tmp.long()
loss = criterion(tmp_dic,label_dic,step)
loss =loss/bs
optimizer.zero_grad()
loss.backward()
optimizer.step()
#scheduler.step()
running_loss.update(loss.item(),bs)
if step%50==0:
#print(torch.cuda.memory_allocated())
#print(torch.cuda.max_memory_cached())
torch.cuda.empty_cache()
#torch.cuda.reset_max_memory_allocated()
print('step:{},now_lr:{} ,loss:{:.4f}({:.4f})'.format(step,now_lr ,running_loss.val,running_loss.avg))
# print('step:{}'.format(step))
show_ref_img = ref_imgs.cpu().numpy()[0]
#show_img1 = img1s.cpu().numpy()[0]
#show_img2 = img2s.cpu().numpy()[0]
mean = np.array([[[0.485]], [[0.456]], [[0.406]]])
sigma = np.array([[[0.229]], [[0.224]], [[0.225]]])
show_ref_img = show_ref_img*sigma+mean
#show_img1 = show_img1*sigma+mean
#show_img2 = show_img2*sigma+mean
#show_gt = label2s.cpu()[0]
show_gt = ref_frame_gts.cpu()[0].squeeze(0).numpy()
show_gtf = label2colormap(show_gt).transpose((2,0,1))
show_scrbble=ref_scribble_labels.cpu()[0].squeeze(0).numpy()
show_scrbble=label2colormap(show_scrbble).transpose((2,0,1))
if r!=0:
show_prev_round_label=prev_round_label.cpu()[0].squeeze(0).numpy()
show_prev_round_label=label2colormap(show_prev_round_label).transpose((2,0,1))
else:
show_prev_round_label = np.zeros_like(show_gt)
show_prev_round_label = label2colormap(show_prev_round_label).transpose((2,0,1))
##########
show_preds = tmp_dic[seq_names[0]].cpu()
show_preds=nn.functional.interpolate(show_preds,size=(h,w),mode = 'bilinear',align_corners=True)
show_preds = show_preds.squeeze(0)
if lossfunc=='bce':
show_preds = show_preds[1:]
show_preds = (torch.sigmoid(show_preds)>0.5)
marker = torch.argmax(show_preds,dim=0)
show_preds_s = torch.zeros((h,w))
for i in range(show_preds.size(0)):
tmp_mask = (marker==i) & (show_preds[i]>0.5)
show_preds_s[tmp_mask]=i+1
elif lossfunc=='cross_entropy':
show_preds_s = torch.argmax(show_preds,dim=0)
show_preds_s = show_preds_s.numpy()
show_preds_sf = label2colormap(show_preds_s).transpose((2,0,1))
pix_acc = np.sum(show_preds_s==show_gt)/(h*w)
if cfg.TRAIN_TBLOG:
tblogger.add_scalar('loss', running_loss.avg, step)
tblogger.add_scalar('pix_acc', pix_acc, step)
tblogger.add_scalar('now_lr', now_lr, step)
tblogger.add_image('Reference image', show_ref_img, step)
#tblogger.add_image('Previous frame image', show_img1, step)
# tblogger.add_image('Current frame image', show_img2, step)
tblogger.add_image('Groud Truth', show_gtf, step)
tblogger.add_image('Predict label', show_preds_sf, step)
tblogger.add_image('Scribble', show_scrbble, step)
tblogger.add_image('prev_round_label', show_prev_round_label, step)
###########TODO
if step%20000==0 and step!=0:
self.save_network(self.model,step)
step+=1
print('trainset evaluating...')
print('*'*100)
if cfg.TRAIN_INTER_USE_TRUE_RESULT:
if r!=round_-1:
if r ==0:
prev_round_label_dic={}
self.model.eval()
with torch.no_grad():
round_scribble={}
frame_num_dic= {}
train_dataset.transform=transforms.Compose([tr.Resize(cfg.DATA_RESCALE),tr.ToTensor()])
# train_dataset.transform=composed_transforms
trainloader = DataLoader(train_dataset,batch_size=1,
sampler = RandomIdentitySampler(train_dataset.sample_list),
shuffle=False,num_workers=cfg.NUM_WORKER,pin_memory=True)
for ii, sample in enumerate(trainloader):
ref_imgs = sample['ref_img'] #batch_size * 3 * h * w
img1s = sample['img1']
img2s = sample['img2']
ref_scribble_labels = sample['ref_scribble_label'] #batch_size * 1 * h * w
label1s = sample['label1']
label2s = sample['label2']
seq_names = sample['meta']['seq_name']
obj_nums = sample['meta']['obj_num']
frame_nums = sample['meta']['frame_num']
bs,_,h,w = img2s.size()
inputs = torch.cat((ref_imgs,img1s,img2s),0)
if r==0:
ref_scribble_labels=self.rough_ROI(ref_scribble_labels)
print(seq_names[0])
# if damage_initial_previous_frame_mask:
# try:
# label1s = damage_masks(label1s)
# except:
# label1s = label1s
# print('damage_error')
label1s_tocat=torch.Tensor()
for i in range(bs):
l = label1s[i]
l = l.unsqueeze(0)
l = mask_damager(l,0.0)
l = torch.from_numpy(l)
l = l.unsqueeze(0).unsqueeze(0)
label1s_tocat = torch.cat((label1s_tocat,l.float()),0)
label1s = label1s_tocat
if self.use_gpu:
inputs = inputs.cuda()
ref_scribble_labels=ref_scribble_labels.cuda()
label1s = label1s.cuda()
tmp_dic, global_map_tmp_dic = self.model(inputs,ref_scribble_labels,label1s,seq_names=seq_names,
gt_ids=obj_nums,k_nearest_neighbors=cfg.KNNS,global_map_tmp_dic=global_map_tmp_dic,
frame_num=frame_nums)
pred_label = tmp_dic[seq_names[0]].detach().cpu()
pred_label = nn.functional.interpolate(pred_label,size=(h,w),mode = 'bilinear',align_corners=True)
pred_label=torch.argmax(pred_label,dim=1)
pred_label= pred_label.unsqueeze(0)
try:
pred_label=damage_masks(pred_label)
except:
pred_label=pred_label
pred_label=pred_label.squeeze(0)
round_scribble[seq_names[0]]=interactor.interact(seq_names[0],pred_label.numpy(),label2s.float().squeeze(0).numpy(),obj_nums)
frame_num_dic[seq_names[0]]=frame_nums[0]
pred_label=pred_label.unsqueeze(0)
img_ww=Image.open(os.path.join(cfg.DATA_ROOT,'JPEGImages/480p/',seq_names[0],'00000.jpg'))
img_ww=np.array(img_ww)
or_h,or_w = img_ww.shape[:2]
pred_label = torch.nn.functional.interpolate(pred_label.float(),(or_h,or_w),mode='nearest')
prev_round_label_dic[seq_names[0]]=pred_label.squeeze(0)
# torch.cuda.empty_cache()
train_dataset.update_ref_frame_and_label(round_scribble,frame_num_dic,prev_round_label_dic)
print('trainset evaluating finished!')
print('*'*100)
self.model.train()
print('updating ref frame and label')
train_dataset.transform=composed_transforms
print('updating ref frame and label finished!')
else:
if r!=round_-1:
round_scribble={}
if r ==0:
prev_round_label_dic={}
# eval_global_map_tmp_dic={}
frame_num_dic= {}
train_dataset.transform=tr.ToTensor()
# train_dataset.transform=tr.ToTensor()
trainloader = DataLoader(train_dataset,batch_size=1,
sampler = RandomIdentitySampler(train_dataset.sample_list),
shuffle=False,num_workers=1,pin_memory=True)
self.model.eval()
with torch.no_grad():
for ii, sample in enumerate(trainloader):
ref_imgs = sample['ref_img'] #batch_size * 3 * h * w
img1s = sample['img1']
img2s = sample['img2']
ref_scribble_labels = sample['ref_scribble_label'] #batch_size * 1 * h * w
label1s = sample['label1']
label2s = sample['label2']
seq_names = sample['meta']['seq_name']
obj_nums = sample['meta']['obj_num']
frame_nums = sample['meta']['frame_num']
bs,_,h,w = img2s.size()
# inputs=torch.cat((ref_imgs,img1s,img2s),0)
# if r==0:
# ref_scribble_labels=self.rough_ROI(ref_scribble_labels)
print(seq_names[0])
label2s_ = mask_damager(label2s,0.1)
# inputs = inputs.cuda()
# ref_scribble_labels=ref_scribble_labels.cuda()
# label1s = label1s.cuda()
# tmp_dic, global_map_tmp_dic = self.model(inputs,ref_scribble_labels,label1s,seq_names=seq_names,
# gt_ids=obj_nums,k_nearest_neighbors=cfg.KNNS,global_map_tmp_dic=global_map_tmp_dic,
# frame_num=frame_nums)
#label2s_show = label2s.squeeze().numpy()
#label2s_im = Image.fromarray(label2s_show.astype('uint8')).convert('P')
#label2s_im.putpalette(_palette)
#label2s_im.save('label.png')
#label2s__show = label2s_
#label2s_im_ = Image.fromarray(label2s__show.astype('uint8')).convert('P')
#label2s_im_.putpalette(_palette)
#label2s_im_.save('damage_label.png')
# exit()
#print(label2s_)
#print(label2s.size())
round_scribble[seq_names[0]]=interactor.interact(seq_names[0],np.expand_dims(label2s_,axis=0),label2s.float().squeeze(0).numpy(),obj_nums)
label2s__=torch.from_numpy(label2s_)
# img_ww=Image.open(os.path.join(cfg.DATA_ROOT,'JPEGImages/480p/',seq_names[0],'00000.jpg'))
# img_ww=np.array(img_ww)
# or_h,or_w = img_ww.shape[:2]
# label2s__=label2s__.unsqueeze(0).unsqueeze(0)
# label2s__ = torch.nn.functional.interpolate(label2s__.float(),(or_h,or_w),mode='nearest')
# label2s__=label2s__.squeeze(0)
# print(label2s__.size())
frame_num_dic[seq_names[0]]=frame_nums[0]
prev_round_label_dic[seq_names[0]]=label2s__
#torch.cuda.empty_cache()
print('trainset evaluating finished!')
print('*'*100)
print('updating ref frame and label')
train_dataset.update_ref_frame_and_label(round_scribble,frame_num_dic,prev_round_label_dic)
self.model.train()
train_dataset.transform=composed_transforms
print('updating ref frame and label finished!')