import torch
from PIL import Image
from torch import nn, save, load
from torch.utils.data import DataLoader
import torchvision
from torchvision import datasets
from torchvision.transforms import ToTensor
import torchvision.transforms as transforms
from torchvision import models
if torch.cuda.is_available():
print(“using CUDA”)
device = ‘cuda’
dtype = torch.float32
elif torch.backends.mps.is_available():
print(“using MPS”)
device = ‘mps’
dtype = torch.float32
else:
print(“using CPU”)
device = ‘cpu’
dtype = torch.float32
transform = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
trainset = torchvision.datasets.CIFAR10(root=‘./data’,train=True, download=True, transform = transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root=‘./data’,train=False, download=True, transform = transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False, num_workers=2)
net = models.resnet50(pretrained=True)
num_ftrs = net.fc.in_features
net.fc = nn.Sequential(
nn.Linear(num_ftrs, 500),
nn.ReLU(),
nn.Linear(500, 300),
nn.Dropout(0.6),
nn.Linear(300,100),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(100,50),
nn.ReLU(), # Corrected here
nn.Dropout(0.35),
nn.Linear(50,25),
nn.ReLU(), # Corrected here
nn.Dropout(0.3),
nn.Linear(25,10),
nn.ReLU() # Corrected here
)
print(“Model Info:”)
print(“ResNet50,Pretrained,weight adj. LR=0.01,Mom=0.3,WD=0.0001”)
print(“Schedule step=1,gamma=0.7, 20 epoches”)
Move the model to the GPU
resnet50v2 = net.to(device, dtype=torch.float32)
optimizer = torch.optim.SGD(resnet50v2.parameters(), lr=0.01,momentum=0.3,weight_decay=0.0001)
criterion = nn.CrossEntropyLoss()
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)
train_losses =
test_losses =
accuracies =
train_acc =
import matplotlib.pyplot as plt
from tqdm import tqdm
for epoch in range(32): # loop over the dataset multiple times
running_loss = 0.0
correctTrain = 0
totalTrain = 0
pbar = tqdm(enumerate(trainloader, 0), total=len(trainloader), desc=“Epoch {}”.format(epoch+1))
for i, data in pbar:
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data[0].to(device,dtype=torch.float32), data[1].to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = resnet50v2(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
_, predicted_train = torch.max(outputs.data, 1)
totalTrain += labels.size(0)
correctTrain += (predicted_train == labels).sum().item()
pbar.set_postfix({'loss': running_loss/(i+1)})
train_accuracy = 100 * correctTrain / totalTrain
train_acc.append(train_accuracy)
print(f'Epoch {epoch + 1} loss: {running_loss / len(trainloader):.3f}')
# Start of testing phase
resnet50v2.eval() # Set the model to evaluation mode
test_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device,dtype=torch.float32), data[1].to(device)
outputs = resnet50v2(images)
loss = criterion(outputs, labels)
test_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Epoch {epoch + 1} Test loss: {test_loss / len(testloader):.3f}, Accuracy: {100 * correct / total:.2f}%')
#print the learning rate
print(f'Epoch {epoch + 1} Learning rate: {optimizer.param_groups[0]["lr"]}')
train_losses.append(running_loss / len(trainloader))
test_losses.append(test_loss / len(testloader))
accuracies.append(100 * correct / total)
resnet50v2.train() # Set the model back to training model
scheduler.step()
print(‘Finished Training’)
plt.figure(figsize=(10, 5))
plt.plot(train_losses, label=‘Training Loss’)
plt.plot(test_losses, label=‘Test Loss’)
plt.xlabel(‘Epochs’)
plt.ylabel(‘Loss’)
plt.legend()
plt.show()
plt.figure(figsize=(10, 5))
plt.plot(accuracies, label=‘Accuracy’)
plt.plot(train_acc, label=‘Training Accuracy’)
plt.xlabel(‘Epochs’)
plt.ylabel(‘Accuracy (%)’)
plt.legend()
plt.show()
print(“Model Info:”)
print(“ResNet50,Pretrained,weight adj. LR=0.01,Mom=0.3,WD=0.0001”)
print(“Schedule step=1,gamma=0.7, 20 epoches”)
