LOOCV Transfer Learning IndexError: list assignment index

#model_ft = model_ft.cuda()
nb_samples = 931
nb_classes = 9


from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

import torch.utils.data as data_utils
from torch.utils import data





data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.RandomRotation(20),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),

        'test': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

val_loader = data.DataLoader(
        image_datasets['test'],
        num_workers=2,
        batch_size=1
    )
val_loader = iter(val_loader)

data_dir = "images"
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'test']}

dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}
print(dataset_sizes)
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# LOOCV
loocv_preds = []
loocv_targets = []
for idx in range(nb_samples):

    print('Using sample {} as test data'.format(idx))

    # Get all indices and remove test sample
    train_indices = list(range(len(image_datasets)))
    del train_indices[idx]

    # Create new sampler
    sampler = data.SubsetRandomSampler(train_indices)

    dataloader = data.DataLoader(
        image_datasets['train'],
        num_workers=2,
        batch_size=1,
        sampler=sampler
    )

    # Train model
    for batch_idx, (samples, target) in enumerate(dataloader):
        print('Batch {}'.format(batch_idx))
        model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25) # do I add this line here?

    # Test on LOO sample
    model_ft.eval()
#    test_data, test_target = image_datasets['train'][idx]
    test_data, test_target = val_loader.next()
    test_data = test_data.cuda()
    test_target = test_target.cuda()
    #test_data.unsqueeze_(1)
    #test_target.unsqueeze_(0)

    output = model_ft(test_data)
    pred = torch.argmax(output, 1)
    loocv_preds.append(pred)
    loocv_targets.append(test_target.item())



-------------------------------------------------------
-------------------------------------------------------
{'train': 791, 'test': 140}
Using sample 0 as test data
Batch 0
Epoch 0/24
----------
train Loss: 2.0170 Acc: 0.3009
test Loss: 2.2370 Acc: 0.3571

Epoch 1/24
----------
train Loss: 2.0436 Acc: 0.3236
test Loss: 1.8071 Acc: 0.4071

Epoch 2/24
----------
train Loss: 1.9560 Acc: 0.3300
test Loss: 1.9911 Acc: 0.3714

Epoch 3/24
----------
train Loss: 1.9052 Acc: 0.3413
test Loss: 2.4123 Acc: 0.3286

Epoch 4/24
----------
train Loss: 1.8917 Acc: 0.3692
test Loss: 2.8163 Acc: 0.3929

Epoch 5/24
----------
train Loss: 1.8890 Acc: 0.3515
test Loss: 2.3379 Acc: 0.3429

Epoch 6/24
----------
train Loss: 1.9090 Acc: 0.3388
test Loss: 2.3404 Acc: 0.3929

Epoch 7/24
----------
train Loss: 1.6069 Acc: 0.4475
test Loss: 2.0714 Acc: 0.4143

Epoch 8/24
----------
train Loss: 1.4701 Acc: 0.5019
test Loss: 2.1390 Acc: 0.4071

Epoch 9/24
----------
train Loss: 1.5322 Acc: 0.4867
test Loss: 2.0545 Acc: 0.4286

Epoch 10/24
----------
train Loss: 1.4743 Acc: 0.5082
test Loss: 2.0200 Acc: 0.4143

Epoch 11/24
----------
train Loss: 1.4498 Acc: 0.5006
test Loss: 2.0408 Acc: 0.4071

Epoch 12/24
----------
train Loss: 1.4314 Acc: 0.5095
test Loss: 2.0807 Acc: 0.4214

Epoch 13/24
----------
train Loss: 1.4097 Acc: 0.5133
test Loss: 2.1612 Acc: 0.4214

Epoch 14/24
----------
train Loss: 1.4116 Acc: 0.5234
test Loss: 2.0989 Acc: 0.4000

Epoch 15/24
----------
train Loss: 1.3736 Acc: 0.5234
test Loss: 2.1113 Acc: 0.4214

Epoch 16/24
----------
train Loss: 1.3562 Acc: 0.5310
test Loss: 2.1046 Acc: 0.4143

Epoch 17/24
----------
train Loss: 1.3730 Acc: 0.5297
test Loss: 2.1644 Acc: 0.4214

Epoch 18/24
----------
train Loss: 1.3351 Acc: 0.5398
test Loss: 2.1070 Acc: 0.4357

Epoch 19/24
----------
train Loss: 1.3228 Acc: 0.5449
test Loss: 2.1646 Acc: 0.4214

Epoch 20/24
----------
train Loss: 1.3837 Acc: 0.5272
test Loss: 2.1686 Acc: 0.4214

Epoch 21/24
----------
train Loss: 1.3377 Acc: 0.5424
test Loss: 2.1626 Acc: 0.4143

Epoch 22/24
----------
train Loss: 1.3879 Acc: 0.5158
test Loss: 2.1593 Acc: 0.4286

Epoch 23/24
----------
train Loss: 1.3329 Acc: 0.5563
test Loss: 2.2069 Acc: 0.4357

Epoch 24/24
----------
train Loss: 1.3181 Acc: 0.5613
test Loss: 2.1064 Acc: 0.4143

Training complete in 5m 5s
Using sample 1 as test data
Batch 0
Epoch 0/24
----------
train Loss: 1.3487 Acc: 0.5310
test Loss: 2.1172 Acc: 0.4286

Epoch 1/24
----------
train Loss: 1.3218 Acc: 0.5474
test Loss: 2.0875 Acc: 0.4214

Epoch 2/24
----------
train Loss: 1.3528 Acc: 0.5436
test Loss: 2.2111 Acc: 0.4286

Epoch 3/24
----------
train Loss: 1.3242 Acc: 0.5499
test Loss: 2.1504 Acc: 0.4286

Epoch 4/24
----------
train Loss: 1.3318 Acc: 0.5499
test Loss: 2.1848 Acc: 0.4286

Epoch 5/24
----------
train Loss: 1.3252 Acc: 0.5449
test Loss: 2.1637 Acc: 0.4357

Epoch 6/24
----------
train Loss: 1.3135 Acc: 0.5424
test Loss: 2.0848 Acc: 0.4357

Epoch 7/24
----------
train Loss: 1.3492 Acc: 0.5373
test Loss: 2.1031 Acc: 0.4071

Epoch 8/24
----------
train Loss: 1.2792 Acc: 0.5575
test Loss: 2.1071 Acc: 0.4286

Epoch 9/24
----------
train Loss: 1.3436 Acc: 0.5386
test Loss: 2.1486 Acc: 0.4500

Epoch 10/24
----------
train Loss: 1.3596 Acc: 0.5373
test Loss: 2.1443 Acc: 0.4214

Epoch 11/24
----------
train Loss: 1.3750 Acc: 0.5499
test Loss: 2.1618 Acc: 0.4214

Epoch 12/24
----------
train Loss: 1.3241 Acc: 0.5424
test Loss: 2.1119 Acc: 0.4214

Epoch 13/24
----------
train Loss: 1.3706 Acc: 0.5335
test Loss: 2.0601 Acc: 0.4143

Epoch 14/24
----------
train Loss: 1.3505 Acc: 0.5525
test Loss: 2.0726 Acc: 0.4214

Epoch 15/24
----------
train Loss: 1.3460 Acc: 0.5550
test Loss: 2.0473 Acc: 0.4429

Epoch 16/24
----------
train Loss: 1.3337 Acc: 0.5563
test Loss: 2.0629 Acc: 0.4143

Epoch 17/24
----------
train Loss: 1.3146 Acc: 0.5601
test Loss: 2.1087 Acc: 0.4214

Epoch 18/24
----------
train Loss: 1.3320 Acc: 0.5512
test Loss: 2.1290 Acc: 0.4286

Epoch 19/24
----------
train Loss: 1.3393 Acc: 0.5601
test Loss: 2.0548 Acc: 0.4143

Epoch 20/24
----------
train Loss: 1.3232 Acc: 0.5474
test Loss: 2.0624 Acc: 0.4429

Epoch 21/24
----------
train Loss: 1.3537 Acc: 0.5386
test Loss: 2.0688 Acc: 0.4286

Epoch 22/24
----------
train Loss: 1.2933 Acc: 0.5664
test Loss: 2.1751 Acc: 0.4143

Epoch 23/24
----------
train Loss: 1.3747 Acc: 0.5424
test Loss: 2.1009 Acc: 0.4143

Epoch 24/24
----------
train Loss: 1.3455 Acc: 0.5183
test Loss: 2.1395 Acc: 0.4143

Training complete in 5m 10s
Using sample 2 as test data

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-10-15f05fc7d5ca> in <module>()
     68     # Get all indices and remove test sample
     69     train_indices = list(range(len(image_datasets)))
---> 70     del train_indices[idx]
     71
     72     # Create new sampler

IndexError: list assignment index out of range