How can I save my training progress in PyTorch for a certain batch no.?
I'm simply trying to train a ResNet18 model using PyTorch library. The training dataset consists of 25,000 images. Therefore, it is taking a lot of time for even the first epoch to complete. Therefore, I want to save the progress after a certain no. of batch iteration is completed. But I can't figure out how to modify my code and how to use the torch.save() and torch.load() functions in my code to save the periodic progress.
My code is given below:

                # BUILD THE NETWORK
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torch.nn.functional as F
import torchvision
import torchvision.models as models
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt

                # DOWNLOAD PRETRAINED MODELS ON ImageNet

model_resnet18 = torch.hub.load('pytorch/vision', 'resnet18', pretrained = True)
model_resnet34 = torch.hub.load('pytorch/vision', 'resnet34', pretrained = True)

for name, param in model_resnet18.named_parameters():
    if('bn' not in name):
        param.requires_grad = False

for name, param in model_resnet34.named_parameters():
    if('bn' not in name):
        param.requires_grad = False

num_classes = 2

model_resnet18.fc = nn.Sequential(nn.Linear(model_resnet18.fc.in_features, 512),
                                  nn.ReLU(),
                                  nn.Dropout(),
                                  nn.Linear(512, num_classes))

model_resnet34.fc = nn.Sequential(nn.Linear(model_resnet34.fc.in_features, 512),
                                  nn.ReLU(),
                                  nn.Dropout(),
                                  nn.Linear(512, num_classes))

                    # FUNCTIONS FOR TRAINING AND LOADING DATA

def train(model, optimizer, loss_fn, train_loader, val_loader, epochs = 5, device = &quot;cuda&quot;):
    print(&quot;Inside Train Function\n&quot;)
    for epoch in range(epochs):
        print(&quot;Epoch : {} running&quot;.format(epoch))
        training_loss = 0.0
        valid_loss = 0.0
        model.train()
        k = 0
        for batch in train_loader:
            optimizer.zero_grad()
            inputs, targets = batch
            inputs = inputs.to(device)
            output = model(inputs)
            loss = loss_fn(output, targets)
            loss.backward()
            optimizer.step()
            training_loss += loss.data.item() * inputs.size(0)
            print(&quot;End of batch loop iteration {} \n&quot;.format(k))
            k = k + 1
        training_loss /= len(train_loader.dataset)

        model.eval()
        num_correct = 0
        num_examples = 0
        for batch in val_loader:
            inputs, targets = batch
            inputs.to(device)
            output = model(inputs)
            targets = targets.to(device)
            loss = loss_fn(output, targets)
            valid_loss += loss.data.item() * inputs.size(0)

            correct = torch.eq(torch.max(F.softmax(output, dim = 1), dim = 1)[1], targets).view(-1)
            num_correct += torch.sum(correct).item()
            num_examples += correct.shape[0]
        valid_loss /= len(val_loader.dataset)

        print('Epoch: {}, Training Loss: {:.4f}, Validation Loss: {:.4f}, accuracy = {:.4f}'.format(epoch, training_loss, valid_loss, num_correct / num_examples))

batch_size = 32
img_dimensions = 224

img_transforms = transforms.Compose([ transforms.Resize((img_dimensions, img_dimensions)),
                                      transforms.ToTensor(),
                                      transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])

img_test_transforms = transforms.Compose([ transforms.Resize((img_dimensions, img_dimensions)),
                                           transforms.ToTensor(),
                                           transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])

def check_image(path):
    try:
        im = Image.open(path)
        return True
    except:
        return False

train_data_path = &quot;E:\Image Recognition\dogsandcats\\train\\&quot;
train_data = torchvision.datasets.ImageFolder(root=train_data_path,transform=img_transforms, is_valid_file=check_image)

validation_data_path = &quot;E:\\Image Recognition\\dogsandcats\\validation\\&quot;   
validation_data = torchvision.datasets.ImageFolder(root=validation_data_path,transform=img_test_transforms, is_valid_file=check_image)

test_data_path = &quot;E:\\Image Recognition\\dogsandcats\\test\\&quot;
test_data = torchvision.datasets.ImageFolder(root=test_data_path,transform=img_test_transforms, is_valid_file=check_image)

num_workers = 6
train_data_loader      = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers)
validation_data_loader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, shuffle=False, num_workers=num_workers)
test_data_loader       = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=num_workers)

print(torch.cuda.is_available(), &quot;\n&quot;)

if torch.cuda.is_available():
    device = torch.device(&quot;cuda&quot;) 
else:
    device = torch.device(&quot;cpu&quot;)

print(f'Num training images: {len(train_data_loader.dataset)}')
print(f'Num validation images: {len(validation_data_loader.dataset)}')
print(f'Num test images: {len(test_data_loader.dataset)}')

def test_model(model):
    print(&quot;Inside Test Model Function\n&quot;)
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_data_loader:
            images, labels = data[0].to(device), data[1].to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('correct: {:d}  total: {:d}'.format(correct, total))
    print('accuracy = {:f}'.format(correct / total))

model_resnet18.to(device)
optimizer = optim.Adam(model_resnet18.parameters(), lr=0.001)
if __name__ == &quot;__main__&quot;:
    train(model_resnet18, optimizer, torch.nn.CrossEntropyLoss(), train_data_loader, validation_data_loader, epochs=2, device=device)
    test_model(model_resnet18)

model_resnet34.to(device)
optimizer = optim.Adam(model_resnet34.parameters(), lr=0.001)
if __name__ == &quot;__main__&quot;:
    train(model_resnet34, optimizer, torch.nn.CrossEntropyLoss(), train_data_loader, validation_data_loader, epochs=2, device=device)
    test_model(model_resnet34)

import os
def find_classes(dir):
    classes = os.listdir(dir)
    classes.sort()
    class_to_idx = {classes[i]: i for i in range(len(classes))}
    return classes, class_to_idx

def make_prediction(model, filename):
    labels, _ = find_classes('E:\\Image Recognition\\dogsandcats\\test\\test')
    img = Image.open(filename)
    img = img_test_transforms(img)
    img = img.unsqueeze(0)
    prediction = model(img.to(device))
    prediction = prediction.argmax()
    print(labels[prediction])
    
  
make_prediction(model_resnet34, 'E:\\Image Recognition\\dogsandcats\\test\\test\\3.jpg') #dog
make_prediction(model_resnet34, 'E:\\Image Recognition\\dogsandcats\\test\\test\\5.jpg') #cat


torch.save(model_resnet18.state_dict(), &quot;./model_resnet18.pth&quot;)
torch.save(model_resnet34.state_dict(), &quot;./model_resnet34.pth&quot;)


# Remember that you must call model.eval() to set dropout and batch normalization layers to
# evaluation mode before running inference. Failing to do this will yield inconsistent inference results.

resnet18 = torch.hub.load('pytorch/vision', 'resnet18')
resnet18.fc = nn.Sequential(nn.Linear(resnet18.fc.in_features,512),nn.ReLU(), nn.Dropout(), nn.Linear(512, num_classes))
resnet18.load_state_dict(torch.load('./model_resnet18.pth'))
resnet18.eval()

resnet34 = torch.hub.load('pytorch/vision', 'resnet34')
resnet34.fc = nn.Sequential(nn.Linear(resnet34.fc.in_features,512),nn.ReLU(), nn.Dropout(), nn.Linear(512, num_classes))
resnet34.load_state_dict(torch.load('./model_resnet34.pth'))
resnet34.eval()

# Test against the average of each prediction from the two models
models_ensemble = [resnet18.to(device), resnet34.to(device)]

correct = 0
total = 0

if __name__ == '__main__':
    with torch.no_grad():
        for data in test_data_loader:
            images, labels = data[0].to(device), data[1].to(device)
            predictions = [i(images).data for i in models_ensemble]
            avg_predictions = torch.mean(torch.stack(predictions), dim=0)
            _, predicted = torch.max(avg_predictions, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()


if total != 0:
    print('accuracy = {:f}'.format(correct / total))
print('correct: {:d}  total: {:d}'.format(correct, total))


To be very precise, I want to save my progress at the end of  for batch in train_loader: loop, for say k = 1500.
If anyone can guide me about modifying my code so that I can save my progress and resume it later, then it will be a great and highly appreciated.

Whenever you want to save your training progress, you need to save two things:

Your model's state dict
Your optimizer's state dict

This can be done in the following way:
def save_checkpoint(model, optimizer, save_path, epoch):
    torch.save({
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'epoch': epoch
    }, save_path)

To resume training, you can restore your model and optimizer's state dict.
def load_checkpoint(model, optimizer, load_path):
    checkpoint = torch.load(load_path)
    model.load_state_dict(checkpoint['model_state_dict'])
    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
    epoch = checkpoint['epoch']
    
    return model, optimizer, epoch

You can save your model at any point in training, wherever you need to. However, it should be ideal to save after finishing an epoch.
