def prepare_data_loader_train_10_splits(texture_train_data_set_path,
texture_train_label_set_path,
texture_val_data_set_path,
texture_val_label_set_path,
texture_batch_size, num_workers,
device):
data_loader_list = []
for i in range(10):
idx = i + 1
print("Split: {0}".format(idx))
texture_train_data_set_path = texture_train_data_set_path.format(idx)
texture_train_label_set_path = texture_train_label_set_path.format(idx)
texture_val_data_set_path = texture_val_data_set_path.format(idx)
texture_val_label_set_path = texture_val_label_set_path.format(idx)
dL = DataLoader()
texture_train_set, train_set_size = dL.get_tensor_set(
texture_train_data_set_path, texture_train_label_set_path, device)
texture_val_set, val_set_size = dL.get_tensor_set(
texture_val_data_set_path, texture_val_label_set_path, device)
print("Train set size: {0}".format(train_set_size))
print("Val set size: {0}".format(val_set_size))
texture_train_data_loader = torch.utils.data.DataLoader(
texture_train_set,
batch_size=texture_batch_size,
shuffle=True,
num_workers=num_workers)
texture_val_data_loader = torch.utils.data.DataLoader(texture_val_set,
num_workers=1,
shuffle=False,
pin_memory=True)
data_loader_dict = {
"train": texture_train_data_loader,
"val": texture_val_data_loader
}
data_loader_list.append(data_loader_dict)
return data_loader_list
def prepare_data_loader_test_10_splits(texture_test_data_set_path,
texture_test_label_set_path, device):
data_loader_list = []
for i in range(10):
idx = i + 1
print("Split: {0}".format(idx))
texture_test_data_set_path = texture_test_data_set_path.format(idx)
texture_test_label_set_path = texture_test_label_set_path.format(idx)
dL = DataLoader()
texture_test_set, test_set_size = dL.get_tensor_set(
texture_test_data_set_path, texture_test_label_set_path, device)
print("Test set size: {0}".format(test_set_size))
test_data_loader = torch.utils.data.DataLoader(texture_test_set,
num_workers=1,
shuffle=False,
pin_memory=True)
data_loader_list.append(test_data_loader)
return data_loader_list
