train_captions.py

import argparse
import torch
import torch.nn as nn
import numpy as np
import os
import pickle
from data_loader import get_caption_loader
from model import EncoderRNN, DecoderRNN
from torch.nn.utils.rnn import pack_padded_sequence
from torchvision import transforms
from build_vocab import Vocabulary


# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def main(args):
    # Create model directory
    if not os.path.exists(args.model_path):
        os.makedirs(args.model_path)

    # Load vocabulary wrapper
    with open(args.vocab_path, 'rb') as f:
        vocab = pickle.load(f)
    
    # Build data loader
    data_loader = get_caption_loader(args.caption_path, vocab, 75,
                                     args.batch_size, shuffle=True,
                                     num_workers=args.num_workers)

    # Build the models
    encoder = EncoderRNN(len(vocab), args.embed_size, args.hidden_size).to(device)
    decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab), args.num_layers).to(device)
    
    # Loss and optimizer
    criterion = nn.CrossEntropyLoss()
    params = list(decoder.parameters()) + list(encoder.embedding.parameters()) + list(encoder.rnn.parameters())
    optimizer = torch.optim.Adam(params, lr=args.learning_rate)
    
    # Train the models
    total_step = len(data_loader)
    for epoch in range(args.num_epochs):
        for i, (captions_src, captions_tgt, lengths) in enumerate(data_loader):
            
            # Set mini-batch dataset
            captions_src = captions_src.to(device)
            captions_tgt = captions_tgt.to(device)
            targets = pack_padded_sequence(captions_tgt, lengths, batch_first=True)[0]
            
            # Forward, backward and optimize
            enc_output, enc_hidden = encoder(captions_src)
            outputs = decoder(enc_hidden[:, -1:, :], captions_tgt, lengths)
            loss = criterion(outputs, targets)
            decoder.zero_grad()
            encoder.zero_grad()
            loss.backward()
            optimizer.step()

            # Print log info
            if i % args.log_step == 0:
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Perplexity: {:5.4f}'
                      .format(epoch, args.num_epochs, i, total_step, loss.item(), np.exp(loss.item()))) 
                
            # Save the model checkpoints
            if (i+1) % args.save_step == 0:
                torch.save(decoder.state_dict(), os.path.join(
                    args.model_path, 'decoder-{}-{}.ckpt'.format(epoch+1, i+1)))
                torch.save(encoder.state_dict(), os.path.join(
                    args.model_path, 'encoder-{}-{}.ckpt'.format(epoch+1, i+1)))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_path', type=str, default='models/' , help='path for saving trained models')
    parser.add_argument('--crop_size', type=int, default=224 , help='size for randomly cropping images')
    parser.add_argument('--vocab_path', type=str, default='data/vocab.pkl', help='path for vocabulary wrapper')
    parser.add_argument('--image_dir', type=str, default='data/resized2014', help='directory for resized images')
    parser.add_argument('--caption_path', type=str, default='data/captions_en5_preprocessed.txt', help='path for train annotation json file')
    parser.add_argument('--log_step', type=int , default=10, help='step size for prining log info')
    parser.add_argument('--save_step', type=int , default=1000, help='step size for saving trained models')
    
    # Model parameters
    parser.add_argument('--embed_size', type=int , default=256, help='dimension of word embedding vectors')
    parser.add_argument('--hidden_size', type=int , default=512, help='dimension of lstm hidden states')
    parser.add_argument('--num_layers', type=int , default=1, help='number of layers in lstm')
    
    parser.add_argument('--num_epochs', type=int, default=5)
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--num_workers', type=int, default=2)
    parser.add_argument('--learning_rate', type=float, default=0.001)
    args = parser.parse_args()
    print(args)
    main(args)