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import argparse
import os
import torch
import torch.nn as nn
import torch.optim as optim
from datasets import load_dataset
from PIL import Image
import numpy as np
from torch.utils.data import DataLoader, Dataset
# Define the MLP model
class MLP(nn.Module):
def __init__(self, input_size, hidden_sizes, output_size):
super(MLP, self).__init__()
layers = []
sizes = [input_size] + hidden_sizes + [output_size]
for i in range(len(sizes) - 1):
layers.append(nn.Linear(sizes[i], sizes[i+1]))
if i < len(sizes) - 2:
layers.append(nn.ReLU())
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
# Custom Dataset class to handle image preprocessing
class TinyImageNetDataset(Dataset):
def __init__(self, dataset):
self.dataset = dataset
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
example = self.dataset[idx]
img = example['image']
img = np.array(img.convert('L')) # Convert PIL image to grayscale NumPy array
img = img.reshape(-1) # Flatten the image
img = torch.from_numpy(img).float() # Convert to tensor
label = torch.tensor(example['label'])
return img, label
# Train the model
def train_model(model, train_loader, val_loader, epochs=10, lr=0.001, save_loss_path=None, save_model_dir=None):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
train_losses = []
val_losses = []
for epoch in range(epochs):
model.train()
running_loss = 0.0
for batch_idx, (inputs, labels) in enumerate(train_loader):
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()
avg_train_loss = running_loss / len(train_loader)
train_losses.append(avg_train_loss)
print(f'Epoch {epoch+1}, Loss: {avg_train_loss}')
# Validation
model.eval()
val_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
val_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
avg_val_loss = val_loss / len(val_loader)
val_losses.append(avg_val_loss)
print(f'Validation Loss: {avg_val_loss}, Accuracy: {100 * correct / total}%')
# Save the model after each epoch
if save_model_dir:
model_path = os.path.join(save_model_dir, f'model_epoch_{epoch+1}.pth')
torch.save(model.state_dict(), model_path)
if save_loss_path:
with open(save_loss_path, 'w') as f:
for epoch, (train_loss, val_loss) in enumerate(zip(train_losses, val_losses)):
f.write(f'Epoch {epoch+1}, Train Loss: {train_loss}, Validation Loss: {val_loss}\n')
return avg_val_loss
# Main function
def main():
parser = argparse.ArgumentParser(description='Train an MLP on the zh-plus/tiny-imagenet dataset.')
parser.add_argument('--layer_count', type=int, default=2, help='Number of hidden layers (default: 2)')
parser.add_argument('--width', type=int, default=512, help='Number of neurons per hidden layer (default: 512)')
parser.add_argument('--batch_size', type=int, default=8, help='Batch size for training (default: 8)')
parser.add_argument('--save_model_dir', type=str, default='saved_models', help='Directory to save model checkpoints (default: saved_models)')
args = parser.parse_args()
# Load the zh-plus/tiny-imagenet dataset
dataset = load_dataset('zh-plus/tiny-imagenet')
# Split the dataset into train and validation sets
train_dataset = dataset['train']
val_dataset = dataset['valid'] # Assuming 'validation' is the correct key
# Determine the number of classes
num_classes = len(set(train_dataset['label']))
# Determine the fixed resolution of the images
image_size = 64 # Assuming the images are square
# Define the model
input_size = image_size * image_size # Since images are grayscale
hidden_sizes = [args.width] * args.layer_count
output_size = num_classes
model = MLP(input_size, hidden_sizes, output_size)
# Create the directory to save models
os.makedirs(args.save_model_dir, exist_ok=True)
# Create DataLoader for training and validation
train_loader = DataLoader(TinyImageNetDataset(train_dataset), batch_size=args.batch_size, shuffle=True)
val_loader = DataLoader(TinyImageNetDataset(val_dataset), batch_size=args.batch_size, shuffle=False)
# Train the model and get the final loss
save_loss_path = 'losses.txt'
final_loss = train_model(model, train_loader, val_loader, save_loss_path=save_loss_path, save_model_dir=args.save_model_dir)
# Calculate the number of parameters
param_count = sum(p.numel() for p in model.parameters())
# Create the folder for the model
model_folder = f'mlp_model_l{args.layer_count}w{args.width}'
os.makedirs(model_folder, exist_ok=True)
# Save the final model
model_path = os.path.join(model_folder, 'model.pth')
torch.save(model.state_dict(), model_path)
# Write the results to a text file in the model folder
result_path = os.path.join(model_folder, 'results.txt')
with open(result_path, 'w') as f:
f.write(f'Layer Count: {args.layer_count}, Width: {args.width}, Parameter Count: {param_count}, Final Loss: {final_loss}\n')
# Save a duplicate of the results in the 'results' folder
results_folder = 'results'
os.makedirs(results_folder, exist_ok=True)
duplicate_result_path = os.path.join(results_folder, f'results_l{args.layer_count}w{args.width}.txt')
with open(duplicate_result_path, 'w') as f:
f.write(f'Layer Count: {args.layer_count}, Width: {args.width}, Parameter Count: {param_count}, Final Loss: {final_loss}\n')
if __name__ == '__main__':
main() |