Trying to resize storage that is not resizable

[Applewine33]

I recond the model then I want to use test data to see whether the model work will it error with the message like title.
How can I get the right result?
Code :
import os
import json
import torch
import matplotlib.pyplot as plt
from monai.networks.nets import UNet
from monai.data import CacheDataset, DataLoader
from monai.inferers import sliding_window_inference
from monai.transforms import (
EnsureChannelFirstd,
Compose,
LoadImaged,
ScaleIntensityRanged,
Spacingd,
)

with open('data_split.json') as f:
data = json.load(f)

testing_set = data["testing"]

testing_transform = Compose([
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys=["image", "label"]),
Spacingd(
keys=["image", "label"],
pixdim=[2.0, 2.0, 2.0]
),
ScaleIntensityRanged(
keys="image",
a_min=-125,
a_max=225,
b_min=0.0,
b_max=1.0
)
])

test_ds = CacheDataset(data=testing_set, transform=testing_transform, num_workers=4)
test_loader = DataLoader(test_ds, batch_size=1, shuffle=False, num_workers=4)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNet(
spatial_dims=3,
in_channels=1,
out_channels=3,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2
).to(device)

model.load_state_dict(torch.load(os.path.join(".", "best_metric_model.pth")))
model.eval()

y_true = []
y_pred = []
with torch.no_grad():
for test_data in test_loader:
test_images, test_labels = (
test_data["image"].to(device),
test_data["label"].to(device),
)
pred = model(test_images).argmax(dim=1)
for i in range(len(pred)):
y_true.append(test_labels[i].item())
y_pred.append(pred[i].item())

print(classification_report(
y_true, y_pred, target_names=class_names, digits=4))

[KumoLiu]

Hi @Applewine33, please share more information such as the whole error message and a small piece of code that can directly reproduce the issue.
Thanks!