train_rl_tb.py

# Use tensorboard

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim

import numpy as np

import time
import os
from six.moves import cPickle

import opts
import models
from dataloader import *
import eval_utils
import misc.utils as utils
import get_rewards
import tensorflow as tf

import os

def add_summary_value(writer, key, value, iteration):
    summary = tf.Summary(value=[tf.Summary.Value(tag=key, simple_value=value)])
    writer.add_summary(summary, iteration)

def train(opt):
    loader = DataLoader(opt)
    opt.vocab_size = loader.vocab_size
    opt.seq_length = loader.seq_length

    tf_summary_writer = tf.summary.FileWriter(opt.checkpoint_path)
    
    infos = {}
    if opt.start_from is not None:
        # open old infos and check if models are compatible
        with open(os.path.join(opt.start_from, 'infos_'+opt.id+'.pkl')) as f:
            infos = cPickle.load(f)
            saved_model_opt = infos['opt']
            need_be_same=["caption_model", "rnn_type", "rnn_size", "num_layers"]
            for checkme in need_be_same:
                assert vars(saved_model_opt)[checkme] == vars(opt)[checkme], "Command line argument and saved model disagree on '%s' " % checkme

    iteration = infos.get('iter', 0)
    epoch = infos.get('epoch', 0)
    val_result_history = infos.get('val_result_history', {})
    loss_history = infos.get('loss_history', {})
    lr_history = infos.get('lr_history', {})
    ss_prob_history = infos.get('ss_prob_history', {})

    loader.iterators = infos.get('iterators', loader.iterators)
    loader.split_ix = infos.get('split_ix', loader.split_ix)
    if opt.load_best_score == 1:
        best_val_score = infos.get('best_val_score', None)

    model = models.setup(opt)
    model.cuda()

    update_lr_flag = True
    # Assure in training mode
    model.train()

    crit = utils.LanguageModelCriterion()
    rl_crit = utils.RewardCriterion()

    optimizer = optim.Adam(model.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay)

    # Load the optimizer
    if vars(opt).get('start_from', None) is not None and os.path.isfile(os.path.join(opt.start_from,"optimizer.pth")):
        optimizer.load_state_dict(torch.load(os.path.join(opt.start_from, 'optimizer.pth')))

    while True:
        if update_lr_flag:
                # Assign the learning rate
            if epoch > opt.learning_rate_decay_start and opt.learning_rate_decay_start >= 0:
                frac = (epoch - opt.learning_rate_decay_start) // opt.learning_rate_decay_every
                decay_factor = opt.learning_rate_decay_rate  ** frac
                opt.current_lr = opt.learning_rate * decay_factor
                utils.set_lr(optimizer, opt.current_lr) # set the decayed rate
            else:
                opt.current_lr = opt.learning_rate
            update_lr_flag = False

        start = time.time()
        # Load data from train split (0)
        data = loader.get_batch('train')
        print('Read data:', time.time() - start)

        torch.cuda.synchronize()
        start = time.time()

        tmp = [data['fc_feats'], data['att_feats']]
        tmp = [Variable(torch.from_numpy(_), requires_grad=False).cuda() for _ in tmp]
        fc_feats, att_feats = tmp
        
        optimizer.zero_grad()

        gen_result, sample_logprobs = model.sample(fc_feats, att_feats, {'sample_max':0})

        rewards = get_rewards.get_self_critical_reward(model, fc_feats, att_feats, data, gen_result)
        loss = rl_crit(sample_logprobs, gen_result, Variable(torch.from_numpy(rewards).float().cuda(), requires_grad=False))

        loss.backward()
        utils.clip_gradient(optimizer, opt.grad_clip)
        optimizer.step()
        train_loss = loss.data[0]
        torch.cuda.synchronize()
        end = time.time()
        print("iter {} (epoch {}), avg_reward = {:.3f}, time/batch = {:.3f}" \
            .format(iteration, epoch, np.mean(rewards[:,0]), end - start))

        # Update the iteration and epoch
        iteration += 1
        if data['bounds']['wrapped']:
            epoch += 1
            update_lr_flag = True

        # Write the training loss summary
        if (iteration % opt.losses_log_every == 0):
            add_summary_value(tf_summary_writer, 'train_loss', train_loss, iteration)
            add_summary_value(tf_summary_writer, 'avg_rewards', np.mean(rewards[:,0]), iteration)
            tf_summary_writer.flush()

            loss_history[iteration] = np.mean(rewards[:,0])
            lr_history[iteration] = opt.current_lr

        # make evaluation on validation set, and save model
        if (iteration % opt.save_checkpoint_every == 0):
            # eval model
            eval_kwargs = {'split': 'val',
                            'dataset': opt.input_json}
            eval_kwargs.update(vars(opt))
            val_loss, predictions, lang_stats = eval_utils.eval_split(model, crit, loader, eval_kwargs)

            # Write validation result into summary
            add_summary_value(tf_summary_writer, 'validation loss', val_loss, iteration)
            for k,v in lang_stats.iteritems():
                add_summary_value(tf_summary_writer, k, v, iteration)
            tf_summary_writer.flush()
            val_result_history[iteration] = {'loss': val_loss, 'lang_stats': lang_stats, 'predictions': predictions}

            # Save model if is improving on validation result
            if opt.language_eval == 1:
                current_score = lang_stats['CIDEr']
            else:
                current_score = - val_loss

            best_flag = False
            if True: # if true
                if best_val_score is None or current_score > best_val_score:
                    best_val_score = current_score
                    best_flag = True
                checkpoint_path = os.path.join(opt.checkpoint_path, 'model.pth')
                torch.save(model.state_dict(), checkpoint_path)
                print("model saved to {}".format(checkpoint_path))
                optimizer_path = os.path.join(opt.checkpoint_path, 'optimizer.pth')
                torch.save(optimizer.state_dict(), optimizer_path)

                # Dump miscalleous informations
                infos['iter'] = iteration
                infos['epoch'] = epoch
                infos['iterators'] = loader.iterators
                infos['split_ix'] = loader.split_ix
                infos['best_val_score'] = best_val_score
                infos['opt'] = opt
                infos['val_result_history'] = val_result_history
                infos['loss_history'] = loss_history
                infos['lr_history'] = lr_history
                infos['ss_prob_history'] = ss_prob_history
                infos['vocab'] = loader.get_vocab()
                with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'.pkl'), 'wb') as f:
                    cPickle.dump(infos, f)

                if best_flag:
                    checkpoint_path = os.path.join(opt.checkpoint_path, 'model-best.pth')
                    torch.save(model.state_dict(), checkpoint_path)
                    print("model saved to {}".format(checkpoint_path))
                    with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'-best.pkl'), 'wb') as f:
                        cPickle.dump(infos, f)

        # Stop if reaching max epochs
        if epoch >= opt.max_epochs and opt.max_epochs != -1:
            break

opt = opts.parse_opt()
train(opt)