working

#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())