I am trying to overfit my data on a mini batch to check if my resnet18 model and training loop is fine or not. Training loss and validation loss are different on same data

Karthik_CS: <pre><code class="lang-auto"># License: BSD
# Author: Sasank Chilamkurthy

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
from tqdm import tqdm
plt.ion()   # interactive mode

mean = [0.5671, 0.5770, 0.5728]
std = [0.1897, 0.1718, 0.1553]
train_transforms = transforms.Compose([
                                transforms.RandomHorizontalFlip(p=0.5),
                                transforms.RandomCrop(1),
                                transforms.RandomRotation(degrees=5), 
                                transforms.Resize((1500, 1500)),
                                transforms.ToTensor(),
                                transforms.Normalize(mean, std)])
test_transforms = transforms.Compose([
                transforms.Resize((1500, 1500)),
                transforms.ToTensor(),
                transforms.Normalize(mean, std)])

num_workers = 0
batch_size = 3

root_train = '/home/dev-25/Occupancy_Type_Image_Classification_NF_CI/image_classification_occupancy_type/Development/archive/flower_data/temp_data'
root_test = '/home/dev-25/Occupancy_Type_Image_Classification_NF_CI/image_classification_occupancy_type/Development/archive/flower_data/temp_data'
train_data = datasets.ImageFolder(root_train, transform=train_transforms)
test_data = datasets.ImageFolder(root_test, transform=test_transforms)

print("Train size:{}".format(len(train_data)))
print("Valid size:{}".format(len(test_data)))

train_loader = torch.utils.data.DataLoader(
    train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers)

test_loader = torch.utils.data.DataLoader(
    test_data, batch_size=batch_size, num_workers=num_workers)

class_names = train_data.classes

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
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', 'val']:
            if phase == 'train':
                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.
            if phase=='train':
                dataloader = test_loader
            elif phase=='val':
                dataloader = test_loader
                
            for inputs, labels in tqdm(dataloader):
                inputs = inputs.cuda()
                labels = labels.cuda()

                # 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)
            if phase == 'train':
                scheduler.step()

            if phase=='train':
                epoch_loss = running_loss / len(train_data)
                epoch_acc = running_corrects.double() / len(train_data)
            
            if phase=='val':
                epoch_loss = running_loss / len(test_data)
                epoch_acc = running_corrects.double() / len(test_data)

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

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

    

    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


model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_ft.fc = nn.Linear(num_ftrs, 3)

model_ft = model_ft.cuda()

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.003)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=250, gamma=0.1)

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                       num_epochs=400)

</code></pre>
<p>I am using resnet18 model with SGD Optimizer with LR 0.03.</p>
<pre><code class="lang-auto">Epoch 253/399
----------
100%|██████████████████████████████████████████████████████████████████| 6/6 [00:02&lt;00:00,  2.01it/s]
train Loss: 0.0130 Acc: 1.0000
100%|██████████████████████████████████████████████████████████████████| 6/6 [00:01&lt;00:00,  4.11it/s]
val Loss: 0.6453 Acc: 0.6250
Epoch 254/399
----------
100%|██████████████████████████████████████████████████████████████████| 6/6 [00:02&lt;00:00,  2.01it/s]
train Loss: 0.0130 Acc: 1.0000
100%|██████████████████████████████████████████████████████████████████| 6/6 [00:01&lt;00:00,  4.10it/s]
val Loss: 0.6454 Acc: 0.6250
Epoch 255/399
----------
100%|██████████████████████████████████████████████████████████████████| 6/6 [00:03&lt;00:00,  1.99it/s]
train Loss: 0.0130 Acc: 1.0000
100%|██████████████████████████████████████████████████████████████████| 6/6 [00:01&lt;00:00,  4.14it/s]
val Loss: 0.6454 Acc: 0.6250
</code></pre>

ptrblck: <p>Why would you expect to see the same training and validation loss?<br>
Assuming you are using <code>model.train()</code> and <code>model.eval()</code> properly, the batchnorm layers might not converged their running stats to the batch statistics and would thus output “not perfectly normalized” data during validation.</p>