Using custom dataset labels don't show up

[CharlieFengCN]

Hi I'm using Unetr to run another public binary classification dataset - ATLAS R2.0 I made sure my json file is fine but why aren't my labels showing up when I do the data visualization This is my process for data extraction and image

`train_transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
ScaleIntensityRanged(
keys=["image"],
a_min=-50,
a_max=100,
b_min=0.0,
b_max=1.0,
clip=True,
),
# Spacingd(keys=["image", "label"], pixdim=(2.0, 2.0, 0.1), mode=("bilinear", "nearest")),
# CropForegroundd(keys=["image", "label"], source_key="image"),
# CenterSpatialCropd(keys=['image', 'label'], roi_size=(512,512,12)),
# Resize(spatial_size=(400, 400, 12)),
ResizeWithPadOrCropd(keys=["image", "label"], spatial_size=(197,233,189)),
RandCropByPosNegLabeld(
keys=["image", "label"],
label_key="label",
spatial_size=(96, 96, 96),
pos=1,
neg=1,
num_samples=8,
image_key="image",
image_threshold=0,
),
# RandAffined(
# keys=["image", "label"],
# mode=("bilinear", "nearest"),
# prob=1.0,
# spatial_size=(512, 512, 16),
# translate_range=(40, 40, 2),
# rotate_range=(np.pi / 36, np.pi / 36, np.pi / 4),
# scale_range=(0.15, 0.15, 0.15),
# padding_mode="border",
# ),
# RandGaussianNoised(keys=["image"], prob=0.10, std=0.1),
RandFlipd(
keys=["image", "label"],
spatial_axis=[0],
prob=0.10,
),
RandFlipd(
keys=["image", "label"],
spatial_axis=[1],
prob=0.10,
),
RandFlipd(
keys=["image", "label"],
spatial_axis=[2],
prob=0.10,
),
RandRotate90d(
keys=["image", "label"],
prob=0.10,
max_k=3,
),
RandShiftIntensityd(
keys=["image"],
offsets=0.10,
prob=0.50,
),
# CenterSpatialCropd(keys=['image', 'label'], roi_size=(352,352,16))
]
)
val_transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
# Spacingd(keys=["image", "label"], pixdim=(1.0, 1.0, 0.1), mode=("bilinear", "nearest")),
ScaleIntensityRanged(
keys=["image"],
a_min=-50,
a_max=100,
b_min=0.0,
b_max=1.0,
clip=True,
),
# CropForegroundd(keys=["image", "label"], source_key="image"),
# Resize(spatial_size=(400, 400, 12)),
ResizeWithPadOrCropd(keys=["image", "label"], spatial_size=(197,233,189)),

    # Rotate90d(keys=["image", "label"], k=1),
    # CenterSpatialCropd(keys=['image', 'label'], roi_size=(352,352,16)),
    # Flipd(keys=["image", "label"], spatial_axis=[0]),
]

) data_dir = r"/home/FCN/code/data"
split_json = "/dataset.json"

datasets = data_dir + split_json
datalist = load_decathlon_datalist(datasets, True, "training")
val_files = load_decathlon_datalist(datasets, True, "validation")
train_ds = CacheDataset(
data=datalist,
transform=train_transforms,
cache_num=24,
cache_rate=1.0,
num_workers=8,
)
device_ids = [i for i in range(torch.cuda.device_count())]
train_loader = DataLoader(train_ds, batch_size=1, shuffle=True, num_workers=0, pin_memory=True)
val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_num=6, cache_rate=1.0, num_workers=4)
val_loader = DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=0, pin_memory=True)

print(len(train_loader))
print(len(val_loader))`

`check_ds = Dataset(data=val_files, transform=val_transforms)
check_loader = DataLoader(check_ds, batch_size=1)
check_data = first(check_loader)
image, label = (check_data["image"][0][0], check_data["label"][0][0])
print(f"image shape: {image.shape}, label shape: {label.shape}")

plot the slice [:, :, 80]

plt.figure("check", (12, 6))
plt.subplot(1, 2, 1)
plt.title("image")
plt.imshow(image[:, :, 80], cmap="gray")
plt.subplot(1, 2, 2)
plt.title("label")
plt.imshow(label[:, :,80])
plt.show()`

Please tell me what's wrong . Thanks very much