def build_dataset_test(root, dataset, crop_size, mode='whole', gt=False):
data_dir = os.path.join(root, dataset)
inform_data_file = os.path.join('./dataset/inform/', dataset + '_inform.pkl')
train_data_list = os.path.join(data_dir, dataset + '_train_list.txt')
if mode == 'whole':
test_data_list = os.path.join(data_dir, dataset + '_test' + '_list.txt')
else:
test_data_list = os.path.join(data_dir, dataset + '_test_sliding' + '_list.txt')
# 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=train_data_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:
datas = pickle.load(open(inform_data_file, "rb"))
class_dict_df = pd.read_csv(os.path.join('./dataset', dataset, 'class_map.csv'))
if dataset == "cityscapes":
# for cityscapes, if test on validation set, set none_gt to False
# if test on the test set, set none_gt to True
if gt:
test_data_list = os.path.join(data_dir, dataset + '_val' + '_list.txt')
testdataset = CityscapesValDataSet(data_dir, test_data_list, crop_size=crop_size, mean=datas['mean'],
std=datas['std'], ignore_label=255)
else:
test_data_list = os.path.join(data_dir, dataset + '_test' + '_list.txt')
testdataset = CityscapesTestDataSet(data_dir, test_data_list, crop_size=crop_size, mean=datas['mean'],
std=datas['std'], ignore_label=255)
return testdataset, class_dict_df
def build_dataset_train(dataset, input_size, batch_size, train_type,
random_scale, random_mirror, num_workers):
data_dir = os.path.join('/media/ding/Data/datasets', dataset)
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=train_data_list,
inform_data_file=inform_data_file)
elif dataset == 'camvid':
dataCollect = CamVidTrainInform(data_dir,
11,
train_set_file=train_data_list,
inform_data_file=inform_data_file)
elif dataset == 'paris':
dataCollect = ParisTrainInform(data_dir,
3,
train_set_file=train_data_list,
inform_data_file=inform_data_file)
elif dataset == 'road':
dataCollect = ParisTrainInform(data_dir,
2,
train_set_file=train_data_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)
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
elif dataset == "paris":
trainLoader = data.DataLoader(ParisDataSet(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(ParisValDataSet(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
elif dataset == "road":
trainLoader = data.DataLoader(RoadDataSet(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(RoadValDataSet(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 build_dataset_sliding_test(dataset, num_workers, none_gt=False):
data_dir = os.path.join('/media/ding/Data/datasets', dataset)
dataset_list = os.path.join(dataset, '_train_list.txt')
if (dataset == 'cityscapes'):
test_data_list = os.path.join(data_dir, dataset + '_val' + '_list.txt')
else:
test_data_list = os.path.join(data_dir,
dataset + '_sliding_test' + '_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)
elif dataset == 'paris':
dataCollect = ParisTrainInform(data_dir,
3,
train_set_file=dataset_list,
inform_data_file=inform_data_file)
# elif dataset == 'austin':
# dataCollect = AustinTrainInform(data_dir, 2, train_set_file=dataset_list,
# inform_data_file=inform_data_file)
elif dataset == 'road':
dataCollect = RoadTrainInform(data_dir,
2,
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":
# for cityscapes, if test on validation set, set none_gt to False
# if test on the test set, set none_gt to True
if not none_gt:
testLoader = data.DataLoader(CityscapesTestDataSet(
data_dir, test_data_list, mean=datas['mean']),
batch_size=1,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
else:
test_data_list = os.path.join(data_dir,
dataset + '_val' + '_list.txt')
testLoader = data.DataLoader(CityscapesValDataSet(
data_dir, test_data_list, mean=datas['mean']),
batch_size=1,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
return datas, testLoader
elif dataset == "camvid":
testLoader = data.DataLoader(CamVidValDataSet(data_dir,
test_data_list,
mean=datas['mean'],
std=datas['std']),
batch_size=1,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
return datas, testLoader
elif dataset == "paris":
testLoader = data.DataLoader(ParisTestDataSet(data_dir,
test_data_list,
mean=datas['mean']),
batch_size=1,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
return datas, testLoader
# elif dataset == "austin":
#
# testLoader = data.DataLoader(
# AustinTestDataSet(data_dir, test_data_list, mean=datas['mean']),
# batch_size=1, shuffle=False, num_workers=num_workers, pin_memory=True)
#
# return datas, testLoader
elif dataset == "road":
testLoader = data.DataLoader(RoadTestDataSet(data_dir,
test_data_list,
mean=datas['mean']),
batch_size=1,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
return datas, testLoader