train.py

import os
import time
import shutil
import torch
import data
from vocab import Vocabulary  # NOQA
from model import VSE

from evaluation import i2t 
from evaluation import t2i
from evaluation import AverageMeter
from evaluation import LogCollector
from evaluation import encode_data

import text_encoders
import logging
import tensorboard_logger as tb_logger
import argparse


def main():
    # Hyper Parameters
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_path', default='/A/VSE/data/',
                        help='path to datasets')
    parser.add_argument('--data_name', default='resnet152_precomp',
                        help='{coco,f8k,f30k,10crop,irv2,resnet152}_precomp|coco|f8k|f30k')
    parser.add_argument('--vocab_path', default='./vocab/',
                        help='Path to saved vocabulary pickle files.')
    parser.add_argument('--margin', default=0.05, type=float,
                        help='Rank loss margin.')
    parser.add_argument('--num_epochs', default=30, type=int,
                        help='Number of training epochs.')
    parser.add_argument('--batch_size', default=128, type=int,
                        help='Size of a training mini-batch.')
    parser.add_argument('--word_dim', default=300, type=int,
                        help='Dimensionality of the word embedding.')
    parser.add_argument('--embed_size', default=1024, type=int,
                        help='Dimensionality of the joint embedding. [NOTE: this is used only if <embed_size> differs from <gru_units>]')
    parser.add_argument('--gru_units', default=1024, type=int,
                        help='Number of GRU neurons.')
    parser.add_argument('--grad_clip', default=1., type=float,
                        help='Gradient clipping threshold.')
    parser.add_argument('--crop_size', default=224, type=int,
                        help='Size of an image crop as the CNN input.')
    parser.add_argument('--num_layers', default=1, type=int,
                        help='Number of GRU layers.')
    parser.add_argument('--learning_rate', default=.001, type=float,
                        help='Initial learning rate.')
    parser.add_argument('--lr_update', default=15, type=int,
                        help='Number of epochs to update the learning rate.')
    parser.add_argument('--workers', default=10, type=int,
                        help='Number of data loader workers.')
    parser.add_argument('--log_step', default=10, type=int,
                        help='Number of steps to print and record the log.')
    parser.add_argument('--val_step', default=500, type=int,
                        help='Number of steps to run validation.')
    parser.add_argument('--logger_name', default='runs/runX',
                        help='Path to save the model and Tensorboard log.')
    parser.add_argument('--resume', default='', type=str, metavar='PATH',
                        help='path to latest checkpoint (default: none)')
    parser.add_argument('--max_violation', action='store_true',
                        help='Use max instead of sum in the rank loss.')
    parser.add_argument('--img_dim', default=2048, type=int,
                        help='Dimensionality of the image embedding.')
    parser.add_argument('--finetune', action='store_true',
                        help='Fine-tune the image encoder.')
    parser.add_argument('--cnn_type', default='vgg19',
                        help="""The CNN used for image encoder
                        (e.g. vgg19, resnet152)""")
    parser.add_argument('--use_restval', action='store_true',
                        help='Use the restval data for training on MSCOCO.')
    parser.add_argument('--measure', default='cosine',
                        help='Similarity measure used (cosine|order)')
    parser.add_argument('--test_measure', default=None,
                        help='Similarity used for retrieval (None<same used for training>|cosine|order)')
    parser.add_argument('--use_abs', action='store_true',
                        help='Take the absolute value of embedding vectors.')
    parser.add_argument('--no_imgnorm', action='store_true',
                        help='Do not normalize the image embeddings.')
    parser.add_argument('--text_encoder', default='seam-e',                        
                        choices=text_encoders.text_encoders_alias.keys())
    parser.add_argument('--att_units', default=300, type=int,
                        help='Number of tanh neurons. When using --att_dim=None we apply a tanh directly to the att input. ')
    parser.add_argument('--att_hops', default=30, type=int,
                        help='Number of attention hops (viewpoints).')
    parser.add_argument('--att_coef', default=0., type=float,
                        help='Influence of Frobenius divergence in the loss function.')
    
    parser.add_argument('--resume2', default='', type=str, metavar='PATH',
                        help='path to latest gan checkpoint (default: none)')
    # parser.add_argument('--gan_coeff', default=0.5, type=float,
                        # help='Trade off coeff for GAN model')

    opt = parser.parse_args()

    if opt.test_measure is None:
        opt.test_measure = opt.measure

    print(opt)

    logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
    tb_logger.configure(opt.logger_name, flush_secs=5)

    tokenizer, vocab_size = data.get_tokenizer(opt.vocab_path, opt.data_name)
    opt.vocab_size = vocab_size

    collate_fn = 'collate_fn'

    # Load data loaders
    train_loader, val_loader = data.get_loaders(
        opt.data_name, tokenizer, opt.crop_size, opt.batch_size, opt.workers, opt, collate_fn)

    # Construct the model
    model = VSE(opt)
    print(model.txt_enc)

    # optionally resume from a checkpoint
    if opt.resume and opt.resume2:
        if os.path.isfile(opt.resume):
            print("=> loading checkpoint 1 '{}'".format(opt.resume))
            checkpoint = torch.load(opt.resume)
            start_epoch = checkpoint['epoch']
            best_rsum = checkpoint['best_rsum']
            print("=> loading checkpoint 2 '{}'".format(opt.resume2))
            checkpoint_2 = torch.load(opt.resume2)

            # model.load_state_dict(checkpoint['model'], checkpoint_2['model']) # se resume2 for .pth.tar
            model.load_state_dict(checkpoint['model'], checkpoint_2) # se resume2 for .pth

            # Eiters is used to show logs as the continuation of another
            # training
            # model.Eiters = checkpoint['Eiters']
            print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})"
                  .format(opt.resume, start_epoch, best_rsum))
            validate(opt, val_loader, model)
        else:
            print("=> no checkpoint found at '{}'".format(opt.resume))

    # Train the Model
    best_rsum = 0
    for epoch in range(opt.num_epochs):
        adjust_learning_rate(opt, model.optimizer, epoch)

        # train for one epoch
        train(opt, train_loader, model, epoch, val_loader)

        # evaluate on validation set
        rsum = validate(opt, val_loader, model)

        # remember best R@ sum and save checkpoint
        is_best = rsum > best_rsum
        best_rsum = max(rsum, best_rsum)
        save_checkpoint({
            'epoch': epoch + 1,
            'model': model.state_dict(),
            'best_rsum': best_rsum,
            'opt': opt,
            'Eiters': model.Eiters,
        }, is_best, prefix=opt.logger_name + '/')


def train(opt, train_loader, model, epoch, val_loader):
    # average meters to record the training statistics
    batch_time = AverageMeter()
    data_time = AverageMeter()
    train_logger = LogCollector()

    # switch to train mode
    model.train_start()

    end = time.time()
    model.epoch = epoch
    for i, train_data in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        # make sure train logger is used
        model.logger = train_logger

        # Update the model
        model.train_emb(*train_data)

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        # Print log info
        if model.Eiters % opt.log_step == 0:
            logging.info(
                'Epoch: [{0}][{1}/{2}]\t'
                '{e_log}\t'
                'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                .format(
                    epoch, i, len(train_loader), batch_time=batch_time,
                    data_time=data_time, e_log=str(model.logger)))

        # Record logs in tensorboard
        tb_logger.log_value('epoch', epoch, step=model.Eiters)
        tb_logger.log_value('step', i, step=model.Eiters)
        tb_logger.log_value('batch_time', batch_time.val, step=model.Eiters)
        tb_logger.log_value('data_time', data_time.val, step=model.Eiters)
        model.logger.tb_log(tb_logger, step=model.Eiters)

        # validate at every val_step
        if model.Eiters % opt.val_step == 0:
            validate(opt, val_loader, model)                        


def _validate(opt, val_loader, model): # NOVO
    # compute the encoding for all the validation images and captions
    img_embs, cap_embs, cap_embs_gan = encode_data(opt, model, val_loader, opt.log_step, logging.info)

    # caption retrieval
    (r1, r5, r10, medr, meanr) = i2t(opt, img_embs, cap_embs, cap_embs_gan, measure=opt.test_measure)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1, r5, r10, medr, meanr))
    # image retrieval
    (r1i, r5i, r10i, medri, meanr) = t2i(opt, img_embs, cap_embs, cap_embs_gan, measure=opt.test_measure)
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1i, r5i, r10i, medri, meanr))
    # sum of recalls to be used for early stopping
    currscore = r1 + r5 + r10 + r1i + r5i + r10i

    # record metrics in tensorboard
    tb_logger.log_value('r1', r1, step=model.Eiters)
    tb_logger.log_value('r5', r5, step=model.Eiters)
    tb_logger.log_value('r10', r10, step=model.Eiters)
    tb_logger.log_value('medr', medr, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('r1i', r1i, step=model.Eiters)
    tb_logger.log_value('r5i', r5i, step=model.Eiters)
    tb_logger.log_value('r10i', r10i, step=model.Eiters)
    tb_logger.log_value('medri', medri, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('rsum', currscore, step=model.Eiters)

    return currscore


def validate(opt, val_loader, model): # ORIGINAL
    # compute the encoding for all the validation images and captions
    img_embs, cap_embs = encode_data(model, val_loader, opt.log_step, logging.info)

    # caption retrieval
    (r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, measure=opt.test_measure)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1, r5, r10, medr, meanr))
    # image retrieval
    (r1i, r5i, r10i, medri, meanr) = t2i(img_embs, cap_embs, measure=opt.test_measure)
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1i, r5i, r10i, medri, meanr))
    # sum of recalls to be used for early stopping
    currscore = r1 + r5 + r10 + r1i + r5i + r10i

    # record metrics in tensorboard
    tb_logger.log_value('r1', r1, step=model.Eiters)
    tb_logger.log_value('r5', r5, step=model.Eiters)
    tb_logger.log_value('r10', r10, step=model.Eiters)
    tb_logger.log_value('medr', medr, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('r1i', r1i, step=model.Eiters)
    tb_logger.log_value('r5i', r5i, step=model.Eiters)
    tb_logger.log_value('r10i', r10i, step=model.Eiters)
    tb_logger.log_value('medri', medri, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('rsum', currscore, step=model.Eiters)

    return currscore


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', prefix=''):
    torch.save(state, prefix + filename)
    if is_best:
        shutil.copyfile(prefix + filename, prefix + 'model_best.pth.tar')


def save_histograms(model, logger_name, curr_iter):
    print 'saving histograms'    
    from matplotlib import pyplot as plt
    plt.switch_backend('agg')

    hist_folder = '/'.join([logger_name, 'histograms'])
    if not os.path.exists(hist_folder):
        os.makedirs(hist_folder)

    filename = '{}/{:07d}.pdf'.format(hist_folder, curr_iter)

    encoder = model.txt_enc
    n_layers = len(encoder.outputs)
    fig, ax = plt.subplots(n_layers, figsize=(10, 4*n_layers))
    for z, (l_name, l_cont) in enumerate(sorted(encoder.outputs.iteritems())):
    #     curr_axis = ax[z%3, z%4]
        curr_axis = ax[z]
        curr_axis.set_title('{}/{}'.format(l_name, l_cont.size()))
        _ = curr_axis.hist(l_cont.data.cpu().numpy().flatten(), bins=100)

    plt.savefig(filename)


def adjust_learning_rate(opt, optimizer, epoch):
    """Sets the learning rate to the initial LR
       decayed by 10 every 30 epochs"""
    lr = opt.learning_rate * (0.1 ** (epoch // opt.lr_update))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


if __name__ == '__main__':
    main()