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train_kld_classifier.py
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train_kld_classifier.py
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import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from copy import deepcopy
from tqdm import tqdm
from torchvision.datasets import FashionMNIST, CIFAR10
from torch.utils.data import DataLoader
from torchvision.models import resnet18
class AddGaussianNoise(object):
def __init__(self, mean=0., std=1.):
self.std = std
self.mean = mean
def __call__(self, tensor):
return tensor + torch.randn(tensor.size()) * self.std + self.mean
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
def get_data_loaders(batch_size=64, dataset='FashionMNIST'):
if dataset == 'FashionMNIST':
transform_train = transforms.Compose([
transforms.Resize(32),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize((0.5,), (0.5,)),
AddGaussianNoise(0., 0.1),
])
transform_test = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize((0.5,), (0.5,))
])
train_dataset = FashionMNIST(root='./data', train=True, transform=transform_train, download=True)
train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [50000, 10000])
test_dataset = FashionMNIST(root='./data', train=False, transform=transform_test, download=True)
elif dataset == 'CIFAR10':
transform_train = transforms.Compose([
transforms.Resize(32),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
AddGaussianNoise(0., 0.1),
])
transform_test = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = CIFAR10(root='./data', train=True, transform=transform_train, download=True)
train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [45000, 5000])
test_dataset = CIFAR10(root='./data', train=False, transform=transform_test, download=True)
else:
raise ValueError(f"Dataset {dataset} not supported.")
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
return train_loader, val_loader, test_loader
def get_model(num_classes=10):
model = resnet18(pretrained='imagenet')
model.fc = nn.Linear(model.fc.in_features, num_classes)
return model
def train_model(model, train_loader, val_loader, num_epochs=10, learning_rate=0.0001, device='cuda'):
best_model = None
best_accuracy = 0.0
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
model.to(device)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
accuracy = 0.0
bar = tqdm(train_loader)
for inputs, labels in bar:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
accuracy += (outputs.argmax(1) == labels).float().mean()
bar.set_description(f"Loss: {loss.item():.4f}, Accuracy: {accuracy.item() / (bar.n + 1):.4f}")
print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(train_loader)}, Accuracy: {accuracy / len(train_loader)}")
# Evaluation
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in tqdm(val_loader):
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
val_accuracy = 100 * correct / total
if val_accuracy > best_accuracy:
best_accuracy = val_accuracy
best_model = deepcopy(model)
print(f"Validation Accuracy: {val_accuracy:.2f}%")
return best_model
def evaluate_model(model, test_loader, device='cuda'):
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in tqdm(test_loader):
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f"Test Accuracy: {100 * correct / total:.2f}%")
def save_model(model, save_folder='./weights/cnn_fmnist'):
os.makedirs(save_folder, exist_ok=True)
save_path = os.path.join(save_folder, 'resnet.pth')
torch.save(model.state_dict(), save_path)
print(f"Model saved at {save_path}")
def main():
parser = argparse.ArgumentParser(description='Train and save a ResNet model on the given dataset.')
parser.add_argument('--batch_size', type=int, default=64, help='Batch size for data loaders')
parser.add_argument('--num_epochs', type=int, default=100, help='Number of epochs for training')
parser.add_argument('--learning_rate', type=float, default=0.0005, help='Learning rate for the optimizer')
parser.add_argument('--dataset', type=str, default='FashionMNIST', help='Dataset to use for training')
parser.add_argument('--device', type=str, default='cuda', help='Device to use for training (cuda or cpu)')
parser.add_argument('--output_path', type=str, default='./weights/cnn_fmnist', help='Path to save the model')
args = parser.parse_args()
train_loader, val_loader, test_loader = get_data_loaders(batch_size=args.batch_size, dataset=args.dataset)
model = get_model()
model = train_model(model, train_loader, val_loader, num_epochs=args.num_epochs,
learning_rate=args.learning_rate, device=args.device)
evaluate_model(model, test_loader, device=args.device)
save_model(model, save_folder=args.output_path)
if __name__ == "__main__":
main()