LOOCV transfer learning

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


def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'test']:
            if phase == 'train':
                scheduler.step()
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
 #           if phase == 'val' and epoch_acc > best_acc:
 #               best_acc = epoch_acc
 #               best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
#    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
#    model.load_state_dict(best_model_wts)
    return model




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=5) # 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
Using sample 1 as test data
Batch 0
Using sample 2 as test data

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-35-4a2b0fe05e20> in <module>()
     65     # Get all indices and remove test sample
     66     train_indices = list(range(len(image_datasets)))
---> 67     del train_indices[idx]
     68
     69     # Create new sampler

IndexError: list assignment index out of range