def build_dataset_train(dataset, input_size, batch_size, train_type, random_scale, random_mirror, num_workers):
data_dir = os.path.join('./dataset/', dataset)
dataset_list = os.path.join(dataset, '_trainval_list.txt')
train_data_list = os.path.join(data_dir, dataset + '_' + train_type + '_list.txt')
val_data_list = os.path.join(data_dir, dataset + '_val' + '_list.txt')
inform_data_file = os.path.join('./dataset/inform/', dataset + '_inform.pkl')
# inform_data_file collect the information of mean, std and weigth_class
if not os.path.isfile(inform_data_file):
print("%s is not found" % (inform_data_file))
if dataset == "cityscapes":
dataCollect = CityscapesTrainInform(data_dir, 19, train_set_file=dataset_list,
inform_data_file=inform_data_file)
elif dataset == 'camvid':
dataCollect = CamVidTrainInform(data_dir, 11, train_set_file=dataset_list,
inform_data_file=inform_data_file)
else:
raise NotImplementedError(
"This repository now supports two datasets: cityscapes and camvid, %s is not included" % dataset)
datas = dataCollect.collectDataAndSave()
if datas is None:
print("error while pickling data. Please check.")
exit(-1)
else:
print("find file: ", str(inform_data_file))
datas = pickle.load(open(inform_data_file, "rb"))
if dataset == "cityscapes":
trainLoader = data.DataLoader(
CityscapesDataSet(data_dir, train_data_list, crop_size=input_size, scale=random_scale,
mirror=random_mirror, mean=datas['mean']),
batch_size=batch_size, shuffle=True, num_workers=num_workers,
pin_memory=True, drop_last=True)
valLoader = data.DataLoader(
CityscapesValDataSet(data_dir, val_data_list, f_scale=1, mean=datas['mean']),
batch_size=1, shuffle=True, num_workers=num_workers, pin_memory=True,
drop_last=True)
return datas, trainLoader, valLoader
elif dataset == "camvid":
trainLoader = data.DataLoader(
CamVidDataSet(data_dir, train_data_list, crop_size=input_size, scale=random_scale,
mirror=random_mirror, mean=datas['mean']),
batch_size=batch_size, shuffle=True, num_workers=num_workers,
pin_memory=True, drop_last=True)
if train_type == 'trainval':
val_data_list = os.path.join(data_dir, dataset + '_test' + '_list.txt')
valLoader = data.DataLoader(
CamVidValDataSet(data_dir, val_data_list, f_scale=1, mean=datas['mean']),
batch_size=1, shuffle=True, num_workers=num_workers, pin_memory=True)
return datas, trainLoader, valLoader
def train_model(args):
"""
args:
args: global arguments
"""
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
print("=====> checking if inform_data_file exists")
if not os.path.isfile(args.inform_data_file):
print("%s is not found" % (args.inform_data_file))
dataCollect = CityscapesTrainInform(
args.data_dir,
args.classes,
train_set_file=args.dataset_list,
inform_data_file=args.inform_data_file
) #collect mean std, weigth_class information
datas = dataCollect.collectDataAndSave()
if datas is None:
print("error while pickling data. Please check.")
exit(-1)
else:
print("find file: ", str(args.inform_data_file))
datas = pickle.load(open(args.inform_data_file, "rb"))
print(args)
global network_type
if args.cuda:
print("=====> use gpu id: '{}'".format(args.gpus))
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
if not torch.cuda.is_available():
raise Exception(
"No GPU found or Wrong gpu id, please run without --cuda")
#args.seed = random.randint(1, 10000)
args.seed = 9830
print("====> Random Seed: ", args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
cudnn.enabled = True
model = MobileNetV3(model_mode="SMALL", num_classes=args.classes)
network_type = "MobileNetV3"
print("=====> current architeture: MobileNetV3")
print("=====> computing network parameters")
total_paramters = netParams(model)
print("the number of parameters: " + str(total_paramters))
print("data['classWeights']: ", datas['classWeights'])
print('=====> Dataset statistics')
print('mean and std: ', datas['mean'], datas['std'])
# define optimization criteria
weight = torch.from_numpy(datas['classWeights'])
criteria = CrossEntropyLoss2d(weight)
if args.cuda:
criteria = criteria.cuda()
if torch.cuda.device_count() > 1:
print("torch.cuda.device_count()=", torch.cuda.device_count())
args.gpu_nums = torch.cuda.device_count()
model = torch.nn.DataParallel(
model).cuda() #multi-card data parallel
else:
print("single GPU for training")
model = model.cuda() #1-card data parallel
args.savedir = (args.savedir + args.dataset + '/' + network_type + 'bs' +
str(args.batch_size) + 'gpu' + str(args.gpu_nums) + "_" +
str(args.train_type) + '/')
if not os.path.exists(args.savedir):
os.makedirs(args.savedir)
train_transform = transforms.Compose([transforms.ToTensor()])
trainLoader = data.DataLoader(CityscapesDataSet(args.data_dir,
args.train_data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=datas['mean']),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
valLoader = data.DataLoader(CityscapesValDataSet(args.data_dir,
args.val_data_list,
f_scale=1,
mean=datas['mean']),
batch_size=1,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
#model.load_state_dict(convert_state_dict(checkpoint['model']))
print("=====> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=====> no checkpoint found at '{}'".format(args.resume))
model.train()
cudnn.benchmark = True
logFileLoc = args.savedir + args.logFile
if os.path.isfile(logFileLoc):
logger = open(logFileLoc, 'a')
logger.write("\nGlobal configuration as follows:")
for key, value in vars(args).items():
logger.write("\n{:16} {}".format(key, value))
logger.write("\nParameters: %s" % (str(total_paramters)))
logger.write(
"\n%s\t\t%s\t\t%s\t\t%s\t\t%s\t\t" %
('Epoch', 'Loss(Tr)', 'Loss(val)', 'mIOU (tr)', 'mIOU (val)'))
else:
logger = open(logFileLoc, 'w')
logger.write("Global configuration as follows:")
for key, value in vars(args).items():
logger.write("\n{:16} {}".format(key, value))
logger.write("\nParameters: %s" % (str(total_paramters)))
logger.write(
"\n%s\t\t%s\t\t%s\t\t%s\t\t%s\t\t" %
('Epoch', 'Loss(Tr)', 'Loss(val)', 'mIOU (tr)', 'mIOU (val)'))
logger.flush()
optimizer = torch.optim.Adam(model.parameters(),
args.lr, (0.9, 0.999),
eps=1e-08,
weight_decay=5e-4)
print('=====> beginning training')
for epoch in range(start_epoch, args.max_epochs):
#training
lossTr, per_class_iu_tr, mIOU_tr, lr = train(args, trainLoader, model,
criteria, optimizer,
epoch)
#validation
if epoch % 50 == 0:
mIOU_val, per_class_iu = val(args, valLoader, model, criteria)
# record train information
logger.write("\n%d\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.7f" %
(epoch, lossTr, mIOU_tr, mIOU_val, lr))
logger.flush()
print("Epoch : " + str(epoch) + ' Details')
print(
"\nEpoch No.: %d\tTrain Loss = %.4f\t mIOU(tr) = %.4f\t mIOU(val) = %.4f\t lr= %.6f"
% (epoch, lossTr, mIOU_tr, mIOU_val, lr))
else:
# record train information
logger.write("\n%d\t\t%.4f\t\t%.4f\t\t%.7f" %
(epoch, lossTr, mIOU_tr, lr))
logger.flush()
print("Epoch : " + str(epoch) + ' Details')
print(
"\nEpoch No.: %d\tTrain Loss = %.4f\t mIOU(tr) = %.4f\t lr= %.6f"
% (epoch, lossTr, mIOU_tr, lr))
#save the model
model_file_name = args.savedir + '/model_' + str(epoch + 1) + '.pth'
state = {"epoch": epoch + 1, "model": model.state_dict()}
if epoch > args.max_epochs - 10:
torch.save(state, model_file_name)
elif not epoch % 20:
torch.save(state, model_file_name)
logger.close()
def build_dataset_train(dataset, input_size, batch_size, train_type, random_scale, random_mirror, num_workers, args):
data_dir = os.path.join('/media/sdb/datasets/segment/', dataset)
dataset_list = dataset + '_trainval_list.txt'
train_data_list = os.path.join(data_dir, dataset + '_' + train_type + '_list.txt')
val_data_list = os.path.join(data_dir, dataset + '_val' + '_list.txt')
inform_data_file = os.path.join('./dataset/inform/', dataset + '_inform.pkl')
if dataset == "cityscapes":
# inform_data_file collect the information of mean, std and weigth_class
if not os.path.isfile(inform_data_file):
print("%s is not found" % (inform_data_file))
dataCollect = CityscapesTrainInform(data_dir, 19, train_set_file=dataset_list,
inform_data_file=inform_data_file)
datas = dataCollect.collectDataAndSave()
if datas is None:
print("error while pickling data. Please check.")
exit(-1)
else:
print("find file: ", str(inform_data_file))
datas = pickle.load(open(inform_data_file, "rb"))
trainLoader = data.DataLoader(
CityscapesDataSet(data_dir, train_data_list, crop_size=input_size, scale=random_scale,
mirror=random_mirror, mean=datas['mean']),
batch_size=batch_size, shuffle=True, num_workers=num_workers,
pin_memory=True, drop_last=True)
valLoader = data.DataLoader(
CityscapesValDataSet(data_dir, val_data_list, f_scale=1, mean=datas['mean']),
batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True,
drop_last=False)
elif dataset == "camvid":
# inform_data_file collect the information of mean, std and weigth_class
if not os.path.isfile(inform_data_file):
print("%s is not found" % (inform_data_file))
dataCollect = CamVidTrainInform(data_dir, 11, train_set_file=dataset_list, inform_data_file=inform_data_file)
datas = dataCollect.collectDataAndSave()
if datas is None:
print("error while pickling data. Please check.")
exit(-1)
else:
print("find file: ", str(inform_data_file))
datas = pickle.load(open(inform_data_file, "rb"))
trainLoader = data.DataLoader(
CamVidDataSet(data_dir, train_data_list, crop_size=input_size, scale=random_scale,
mirror=random_mirror, mean=datas['mean']),
batch_size=batch_size, shuffle=True, num_workers=num_workers,
pin_memory=True, drop_last=True)
valLoader = data.DataLoader(
CamVidValDataSet(data_dir, val_data_list, f_scale=1, mean=datas['mean']),
batch_size=1, shuffle=True, num_workers=num_workers, pin_memory=True)
elif dataset == "ade20k":
inform_data_file = os.path.join('./dataset/inform/', 'cityscapes_inform.pkl')
datas = pickle.load(open(inform_data_file, "rb"))
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
data_kwargs = {'transform': input_transform, 'base_size': args.input_size[0]+40,
'crop_size': args.input_size[0], 'encode': False}
train_dataset = ADE20KSegmentation(split='train', mode='train', **data_kwargs)
val_dataset = ADE20KSegmentation(split='val', mode='val', **data_kwargs)
train_sampler = make_data_sampler(train_dataset, shuffle=True, distributed=False)
train_batch_sampler = make_batch_data_sampler(train_sampler, batch_size)
val_sampler = make_data_sampler(val_dataset, shuffle=False, distributed=False)
val_batch_sampler = make_batch_data_sampler(val_sampler, batch_size)
trainLoader = data.DataLoader(dataset=train_dataset,
batch_sampler=train_batch_sampler,
num_workers=args.num_workers,
pin_memory=True)
valLoader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.num_workers,
pin_memory=True)
else:
raise NotImplementedError(
"This repository now supports datasets: cityscapes, camvid and ade20k, %s is not included" % dataset)
return datas, trainLoader, valLoader