trainer_bert_vatex_classify.py

from __future__ import print_function
import pickle
import os
import sys
import time
import shutil
import json
import numpy as np
import torch
import evaluation_vatex_classify
import util.data_provider as data
from util.vocab import Vocabulary
from util.text2vec import get_text_encoder
from model_part.model_vatex_fine_classify import get_model
from util.data_classify import DatasetCorrelation, collate_data, DatasetCorrelationVal

import logging
import tensorboard_logger as tb_logger
import argparse

from basic.constant import ROOT_PATH
from basic.bigfile import BigFile
from basic.common import makedirsforfile, checkToSkip
from basic.util import read_dict, AverageMeter, LogCollector
from basic.generic_utils import Progbar

INFO = __file__


def parse_args():
    # Hyper Parameters
    parser = argparse.ArgumentParser()
    parser.add_argument('--runpath', type=str, default='/home/fengkai/PycharmProjects/dual_encoding/result/')
    parser.add_argument('--trainTextCollection', type=str, default='vatex/text_embed_info/train_mean_multi_np', help='train collection')
    parser.add_argument('--valTextCollection', type=str, default='vatex/text_embed_info/val_mean_multi_np', help='validation collection')
    parser.add_argument('--testTextCollection', type=str, default='vatex/text_embed_info/test',  help='test collection')
    parser.add_argument('--trainVideoCollection', type=str, default='vatex/video_embed_info/train_video', help='train collection')
    parser.add_argument('--valVideoCollection', type=str, default='vatex/video_embed_info/val_video', help='validation collection')
    parser.add_argument('--testVideoCollection', type=str, default='vatex/video_embed_info/test_video',  help='test collection')
    parser.add_argument('--classify_csv', type=str, default='vatex/video_text_classify.csv',  help='label path')
    parser.add_argument('--n_caption', type=int, default=1, help='number of captions of each image/video (default: 1)')
    parser.add_argument('--overwrite', type=int, default=0, choices=[0,1], help='overwrite existed file. (default: 0)')
    # model
    parser.add_argument('--model', type=str, default='dual_encoding', help='model name. (default: dual_encoding)')
    parser.add_argument('--concate', type=str, default='full', help='feature concatenation style. (full|reduced) full=level 1+2+3; reduced=level 2+3')
    parser.add_argument('--measure', type=str, default='cosine', help='measure method. (default: cosine)')
    parser.add_argument('--dropout', default=0.2, type=float, help='dropout rate (default: 0.2)')
    # text-side multi-level encoding
    parser.add_argument('--vocab', type=str, default='word_vocab_5', help='word vocabulary. (default: word_vocab_5)')
    parser.add_argument('--word_dim', type=int, default=768, help='word embedding dimension')
    parser.add_argument('--text_rnn_size', type=int, default=1024, help='text rnn encoder size. (default: 1024)')
    parser.add_argument('--text_kernel_num', default=512, type=int, help='number of each kind of text kernel')
    parser.add_argument('--text_kernel_sizes', default='2-3-4', type=str, help='dash-separated kernel size to use for text convolution')
    parser.add_argument('--text_norm', action='store_false', help='normalize the text embeddings at last layer')
    # video-side multi-level encoding
    parser.add_argument('--visual_rnn_size', type=int, default=1024, help='visual rnn encoder size')
    parser.add_argument('--visual_feat_dim', type=int, default=1024, help='visual feature size')
    parser.add_argument('--visual_kernel_num', default=512, type=int, help='number of each kind of visual kernel')
    parser.add_argument('--visual_kernel_sizes', default='2-3-4-5', type=str, help='dash-separated kernel size to use for visual convolution')
    parser.add_argument('--visual_norm', action='store_false', help='normalize the visual embeddings at last layer')
    # common space learning
    parser.add_argument('--text_mapping_layers', type=str, default='0-2048', help='text fully connected layers for common space learning. (default: 0-2048)')
    parser.add_argument('--visual_mapping_layers', type=str, default='0-2048', help='visual fully connected layers  for common space learning. (default: 0-2048)')
    # loss
    parser.add_argument('--loss_fun', type=str, default='mrl', help='loss function')
    parser.add_argument('--margin', type=float, default=0.2, help='rank loss margin')
    parser.add_argument('--direction', type=str, default='all', help='retrieval direction (all|t2i|i2t)')
    parser.add_argument('--max_violation', action='store_true', help='use max instead of sum in the rank loss')
    parser.add_argument('--cost_style', type=str, default='sum', help='cost style (sum, mean). (default: sum)')
    # optimizer
    parser.add_argument('--optimizer', type=str, default='adam', help='optimizer. (default: rmsprop)')
    parser.add_argument('--learning_rate', type=float, default=0.0001, help='initial learning rate')
    parser.add_argument('--lr_decay_rate', default=0.99, type=float, help='learning rate decay rate. (default: 0.99)')
    parser.add_argument('--grad_clip', type=float, default=2, help='gradient clipping threshold')
    parser.add_argument('--resume', default='/home/fengkai//model_best.pth.tar', type=str, metavar='PATH', help='path to latest checkpoint (default: none)')
    parser.add_argument('--val_metric', default='recall', type=str, help='performance metric for validation (mir|recall)')
    # misc
    parser.add_argument('--num_epochs', default=100, 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('--workers', default=2, type=int, help='Number of data loader workers.')
    parser.add_argument('--postfix', default='runs_4_att_auc', help='Path to save the model and Tensorboard log.')
    parser.add_argument('--log_step', default=10, type=int, help='Number of steps to print and record the log.')
    parser.add_argument('--cv_name', default='fengkai_vatex_classify', type=str, help='')

    args = parser.parse_args()
    return args


def main():
    opt = parse_args()
    print(json.dumps(vars(opt), indent = 2))
    rootpath = ROOT_PATH
    trainVideoCollection = opt.trainVideoCollection
    valVideoCollection = opt.valVideoCollection
    trainTextCollection = opt.trainTextCollection
    valTextCollection = opt.valTextCollection
    flag_csv = os.path.join(rootpath, opt.classify_csv)


    if opt.loss_fun == "mrl" and opt.measure == "cosine":
        assert opt.text_norm is True
        assert opt.visual_norm is True

    # checkpoint path
    model_info = '%s_concate_%s_dp_%.1f_measure_%s' %  (opt.model, opt.concate, opt.dropout, opt.measure)
    # text-side multi-level encoding info
    text_encode_info = 'vocab_%s_word_dim_%s_text_rnn_size_%s_text_norm_%s' % \
            (opt.vocab, opt.word_dim, opt.text_rnn_size, opt.text_norm)
    text_encode_info += "_kernel_sizes_%s_num_%s" % (opt.text_kernel_sizes, opt.text_kernel_num)
    # video-side multi-level encoding info
    visual_encode_info = 'visual_feat_dim_%s_visual_rnn_size_%d_visual_norm_%s' % \
            (opt.visual_feat_dim, opt.visual_rnn_size, opt.visual_norm)
    visual_encode_info += "_kernel_sizes_%s_num_%s" % (opt.visual_kernel_sizes, opt.visual_kernel_num)
    # common space learning info
    mapping_info = "mapping_text_%s_img_%s" % (opt.text_mapping_layers, opt.visual_mapping_layers)
    loss_info = 'loss_func_%s_margin_%s_direction_%s_max_violation_%s_cost_style_%s' % \
                    (opt.loss_fun, opt.margin, opt.direction, opt.max_violation, opt.cost_style)
    optimizer_info = 'optimizer_%s_lr_%s_decay_%.2f_grad_clip_%.1f_val_metric_%s' % \
                    (opt.optimizer, opt.learning_rate, opt.lr_decay_rate, opt.grad_clip, opt.val_metric)
    runpath = opt.runpath
    opt.logger_name = os.path.join(runpath, opt.cv_name, model_info, text_encode_info,
                            visual_encode_info, mapping_info, loss_info, optimizer_info, opt.postfix)
    print(opt.logger_name)

    if checkToSkip(os.path.join(opt.logger_name, 'model_best.pth.tar'), opt.overwrite):
        sys.exit(0)
    if checkToSkip(os.path.join(opt.logger_name, 'val_metric.txt'), opt.overwrite):
        sys.exit(0)
    makedirsforfile(os.path.join(opt.logger_name, 'val_metric.txt'))
    logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
    tb_logger.configure(opt.logger_name, flush_secs=5)


    opt.text_kernel_sizes = list(map(int, opt.text_kernel_sizes.split('-')))
    opt.visual_kernel_sizes = list(map(int, opt.visual_kernel_sizes.split('-')))
    # collections: trian, val
    collections_video = {'train_video': trainVideoCollection, 'val_video': valVideoCollection}
    collections_text = {'train_text': trainTextCollection, 'val_text': valTextCollection}
    # caption
    caption_files = { x: os.path.join(rootpath, collections_text[x])for x in collections_text }
    # Load visual features
    visual_feat_path = {x: os.path.join(rootpath, collections_video[x])for x in collections_video }

    # set data loader
    dset = {'train': DatasetCorrelation(caption_files['train_text'], visual_feat_path['train_video']),
            'val': DatasetCorrelationVal(caption_files['val_text'], visual_feat_path['val_video'],flag_csv) }
    data_loaders_train = torch.utils.data.DataLoader(dataset=dset['train'],
                                    batch_size=opt.batch_size,
                                    shuffle=True,
                                    pin_memory=True,
                                    num_workers=opt.workers,
                                    collate_fn = collate_data)
    data_loaders_val = torch.utils.data.DataLoader(dataset=dset['val'],
                                    batch_size=opt.batch_size,
                                    shuffle=False,
                                    pin_memory=True,
                                    num_workers=opt.workers,
                                    collate_fn = collate_data)
                        

    # Construct the model
    model = get_model(opt.model)(opt)
    opt.we_parameter = None
    
    # optionally resume from a checkpoint
    if opt.resume:
        if os.path.isfile(opt.resume):
            print("=> loading checkpoint '{}'".format(opt.resume))
            checkpoint = torch.load(opt.resume)
            start_epoch = checkpoint['epoch']
            best_rsum = checkpoint['best_rsum']
            pretrained_dict = checkpoint['model']
            model_dict = model.state_dict()
            for i in range(2):
                pretrained_dict_sw =  {k: v for k, v in pretrained_dict[i].items() if k in model_dict[i]} 
                model_dict[i].update(pretrained_dict_sw) 
            model.load_state_dict(model_dict) 
            # model.load_state_dict(checkpoint['model'])
            # Eiters is used to show logs as the continuation of another
            # training
            model.Eiters = checkpoint['Eiters']
            # TODO 锁定前面的层
            print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})"
                  .format(opt.resume, start_epoch, best_rsum))

    model.classification.cuda()
    # Train the Model
    best_rsum = 0
    no_impr_counter = 0
    lr_counter = 0 
    best_epoch = None
    fout_val_metric_hist = open(os.path.join(opt.logger_name, 'val_metric_hist.txt'), 'w')
    for epoch in range(opt.num_epochs):
        print('Epoch[{0} / {1}] LR: {2}'.format(epoch, opt.num_epochs, get_learning_rate(model.optimizer)[0]))
        print('-'*10)
        # train for one epoch
        train(opt, data_loaders_train, model, epoch)

        # evaluate on validation set
        acc, pre, recall, f1, auc = validate(opt, data_loaders_val, model, measure=opt.measure)

        # remember best R@ sum and save checkpoint
        is_best = (auc) > best_rsum
        best_rsum = max((auc), best_rsum)
        print(' ** score ** acc:{}\t pre:{}\t recall:{}\t f1:{}\t auc:{} '.format(acc, pre, recall, f1, auc))
        print(' * Current perf: {}'.format(auc))
        print(' * Best perf: {}'.format(best_rsum))
        print('')
        fout_val_metric_hist.write('epoch_%d: %f\n' % (epoch, f1))
        fout_val_metric_hist.flush()
        
        if is_best:
            save_checkpoint({
                'epoch': epoch + 1,
                'model': model.state_dict(),
                'best_rsum': best_rsum,
                'opt': opt,
                'Eiters': model.Eiters,
            }, is_best, filename='checkpoint_epoch_%s.pth.tar'%epoch, prefix=opt.logger_name + '/', best_epoch=best_epoch)
            best_epoch = epoch

        lr_counter += 1
        decay_learning_rate(opt, model.optimizer, opt.lr_decay_rate)
        if not is_best:
            # Early stop occurs if the validation performance does not improve in ten consecutive epochs
            no_impr_counter += 1
            if no_impr_counter > 10:
                print('Early stopping happended.\n')
                break

            # When the validation performance decreased after an epoch,
            # we divide the learning rate by 2 and continue training;
            # but we use each learning rate for at least 3 epochs.
            if lr_counter > 2:
                decay_learning_rate(opt, model.optimizer, 0.5)
                lr_counter = 0
        else:
            no_impr_counter = 0

    fout_val_metric_hist.close()

    print('best performance on validation: {}\n'.format(best_rsum))
    with open(os.path.join(opt.logger_name, 'val_metric.txt'), 'w') as fout:
        fout.write('best performance on validation: ' + str(best_rsum))



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

    # switch to train mode
    model.train_start()

    progbar = Progbar(len(train_loader.dataset))
    end = time.time()
    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
        b_size, loss = model.train_emb(*train_data)

        progbar.add(b_size, values=[('loss', loss)])

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

        # 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)


def validate(opt, val_loader, model, measure='cosine'):
    # compute the encoding for all the validation video and captions
    scores = evaluation_vatex_classify.encode_data(model, val_loader, opt.log_step, logging.info)
    acc, pre, recall, f1, auc = scores
    # record metrics in tensorboard
    tb_logger.log_value('acc', acc, step=model.Eiters)
    tb_logger.log_value('pre', pre, step=model.Eiters)
    tb_logger.log_value('recall', recall, step=model.Eiters)
    tb_logger.log_value('f1', f1, step=model.Eiters)
    tb_logger.log_value('auc', f1, step=model.Eiters)

    return scores



def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', prefix='', best_epoch=None):
    """save checkpoint at specific path"""
    torch.save(state, prefix + filename)
    if is_best:
        shutil.copyfile(prefix + filename, prefix + 'model_best.pth.tar')
    if best_epoch is not None:
        os.remove(prefix + 'checkpoint_epoch_%s.pth.tar'%best_epoch)

def decay_learning_rate(opt, optimizer, decay):
    """decay learning rate to the last LR"""
    for param_group in optimizer.param_groups:
        param_group['lr'] = param_group['lr']*decay

def get_learning_rate(optimizer):
    """Return learning rate"""
    lr_list = []
    for param_group in optimizer.param_groups:
        lr_list.append(param_group['lr'])
    return lr_list


if __name__ == '__main__':
    main()