pgcn_train.py

from tensorboardX import SummaryWriter
import os
import time
import shutil
import torch
import random
import torchvision
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.nn.utils import clip_grad_norm
import numpy as np
# from pgcn_dataset import PGCNDataSet
from dataset import PGCNDataSet, collate_fn
from pgcn_models import PGCN
from pgcn_opts import parser
from ops.pgcn_ops import CompletenessLoss, ClassWiseRegressionLoss
from ops.utils import get_and_save_args, get_logger
from tools.Recorder import Recorder
#from tensorboardX import SummaryWriter

SEED = 777
random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
np.random.seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

best_loss = 100
cudnn.benchmark = True
pin_memory = True
os.environ['CUDA_DEVICE_ORDER'] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = "6"

def main():
    global args, best_loss, writer, adj_num, logger

    configs = get_and_save_args(parser)
    parser.set_defaults(**configs)
    dataset_configs = configs["dataset_configs"]
    model_configs = configs["model_configs"]
    graph_configs = configs["graph_configs"]
    args = parser.parse_args()

    """copy codes and creat dir for saving models and logs"""
    if not os.path.isdir(args.snapshot_pref):
        os.makedirs(args.snapshot_pref)

    logger = get_logger(args)
    logger.info('\ncreating folder: ' + args.snapshot_pref)

    if (not args.evaluate) and (not args.debug):
        writer = SummaryWriter(args.snapshot_pref)
        recorder = Recorder(args.snapshot_pref, ["exps", "__pycache__"])
        recorder.writeopt(args)

    logger.info('\nruntime args\n\n{}\n\nconfig\n\n{}'.format(args, dataset_configs))

    train_dataset = PGCNDataSet(dataset_configs, graph_configs,
                                prop_file=dataset_configs['train_prop_file'],
                                prop_dict_path=dataset_configs['train_dict_path'],
                                ft_path=dataset_configs['train_ft_path'],
                                epoch_multiplier=dataset_configs['training_epoch_multiplier'],
                                mode='Train')
    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn,
                                               num_workers=8, pin_memory=True)

    val_dataset = PGCNDataSet(dataset_configs, graph_configs,
                                prop_file=dataset_configs['test_prop_file'],
                                prop_dict_path=dataset_configs['val_dict_path'],
                                ft_path=dataset_configs['test_ft_path'],
                                epoch_multiplier=dataset_configs['testing_epoch_multiplier'],
                                mode='Val')

    val_loader = torch.utils.data.DataLoader(val_dataset,
                                               batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn,
                                               num_workers=8, pin_memory=True)

    """construct model"""
    act_iou_dict, act_dis_dict = train_dataset.act_iou_dict, train_dataset.act_dis_dict
    model = PGCN(model_configs, graph_configs, dataset_configs)

    policies = model.get_optim_policies()
    model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()

    if args.resume:
        if os.path.isfile(args.resume):
            logger.info(("=> loading checkpoint '{}'".format(args.resume)))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_loss = checkpoint['best_loss']
            model.load_state_dict(checkpoint['state_dict'])
            logger.info(("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.evaluate, checkpoint['epoch'])))
        else:
            logger.info(("=> no checkpoint found at '{}'".format(args.resume)))

    """construct dataset"""
    # train_loader = torch.utils.data.DataLoader(
    #     PGCNDataSet(dataset_configs, graph_configs,
    #                 prop_file=dataset_configs['train_prop_file'],
    #                 prop_dict_path=dataset_configs['train_dict_path'],
    #                 ft_path=dataset_configs['train_ft_path'],
    #                 epoch_multiplier=dataset_configs['training_epoch_multiplier'],
    #                 test_mode=False),
    #     batch_size=args.batch_size, shuffle=True,
    #     num_workers=args.workers, pin_memory=True, drop_last=True)  # in training we drop the last incomplete minibatch

    # val_loader = torch.utils.data.DataLoader(
    #     PGCNDataSet(dataset_configs, graph_configs,
    #                 prop_file=dataset_configs['test_prop_file'],
    #                 prop_dict_path=dataset_configs['val_dict_path'],
    #                 ft_path=dataset_configs['test_ft_path'],
    #                 epoch_multiplier=dataset_configs['testing_epoch_multiplier'],
    #                 reg_stats=train_loader.dataset.stats,
    #                 test_mode=False),
    #     batch_size=args.batch_size, shuffle=False,
    #     num_workers=args.workers, pin_memory=True)

    """loss and optimizer"""
    activity_criterion = torch.nn.CrossEntropyLoss().cuda()
    completeness_criterion = CompletenessLoss().cuda()
    regression_criterion = ClassWiseRegressionLoss().cuda()

    for group in policies:
        logger.info(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
            group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))

    optimizer = torch.optim.SGD(policies,
                                args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    if args.evaluate:
        validate(val_loader, model, activity_criterion, completeness_criterion, regression_criterion, 0)
        return


    for epoch in range(args.start_epoch, args.epochs):

        adjust_learning_rate(optimizer, epoch, args.lr_steps)
        train(train_loader, model, activity_criterion, completeness_criterion, regression_criterion, optimizer, epoch)

        # evaluate on validation set
        if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
            loss = validate(val_loader, model, activity_criterion, completeness_criterion, regression_criterion, (epoch + 1) * len(train_loader))
            # remember best validation loss and save checkpoint
            is_best = loss < best_loss
            best_loss = min(loss, best_loss)
            save_checkpoint({
                'epoch': epoch + 1,
                'state_dict': model.state_dict(),
                'best_loss': loss,
                'reg_stats': torch.from_numpy(train_loader.dataset.stats)
            }, is_best, epoch, filename='checkpoint.pth.tar')

    writer.close()


def train(train_loader, model, act_criterion, comp_criterion, regression_criterion, optimizer, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    act_losses = AverageMeter()
    comp_losses = AverageMeter()
    reg_losses = AverageMeter()
    act_accuracies = AverageMeter()
    fg_accuracies = AverageMeter()
    bg_accuracies = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    optimizer.zero_grad()

    ohem_num = train_loader.dataset.fg_per_video
    comp_group_size = train_loader.dataset.fg_per_video + train_loader.dataset.incomplete_per_video
    for i, (batch_act_fts, batch_comp_fts, batch_props_type,
            batch_props_label, batch_reg_target, props_num_list, vid_list) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        batch_size = batch_act_fts.size(0)
        activity_out, activity_target, activity_prop_type, \
        completeness_out, completeness_target, \
        regression_out, regression_labels, regression_target, ohem_num = model(batch_act_fts, batch_comp_fts,
                                                                               batch_props_type,
                                                                               batch_props_label, batch_reg_target,
                                                                        props_num_list, vid_list, True)
        try:
            act_loss = act_criterion(activity_out, activity_target)
        except:
            a = 0
        comp_loss = comp_criterion(completeness_out, completeness_target, ohem_num, 7 * ohem_num)
        reg_loss = regression_criterion(regression_out, regression_labels, regression_target)

        loss = act_loss + comp_loss * args.comp_loss_weight + reg_loss * args.reg_loss_weight

        losses.update(loss.item(), batch_size)
        act_losses.update(act_loss.item(), batch_size)
        comp_losses.update(comp_loss.item(), batch_size)
        reg_losses.update(reg_loss.item(), batch_size)

        act_acc = accuracy(activity_out, activity_target)
        act_accuracies.update(act_acc[0].item(), activity_out.size(0))

        fg_indexer = (activity_prop_type == 0).nonzero().squeeze()\
            if len((activity_prop_type == 0).nonzero()) != 1 else (activity_prop_type == 0).nonzero().squeeze(-1)
        bg_indexer = (activity_prop_type == 2).nonzero().squeeze()\
            if len((activity_prop_type == 2).nonzero()) != 1 else (activity_prop_type == 2).nonzero().squeeze(-1)

        fg_acc = accuracy(activity_out[fg_indexer], activity_target[fg_indexer])
        fg_accuracies.update(fg_acc[0].item(), len(fg_indexer))
        #
        if len(bg_indexer) > 0:
            bg_acc = accuracy(activity_out[bg_indexer, :], activity_target[bg_indexer])
            bg_accuracies.update(bg_acc[0].item(), len(bg_indexer))

        loss.backward()

        if i % args.iter_size == 0:
            # scale down gradients when iter size is functioning
            if args.iter_size != 1:
                for g in optimizer.param_groups:
                    for p in g['params']:
                        p.grad /= args.iter_size

            if args.clip_gradient is not None:
                total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
                if total_norm > args.clip_gradient:
                    logger.info("clipping gradient: {} with coef {}".format(total_norm, args.clip_gradient / total_norm))
            else:
                total_norm = 0

            optimizer.step()
            optimizer.zero_grad()

        batch_time.update(time.time() - end)
        end = time.time()

        writer.add_scalar('data/loss', losses.val, epoch*len(train_loader)+i+1)
        writer.add_scalar('data/Reg_loss', reg_losses.val, epoch*len(train_loader)+i+1)
        writer.add_scalar('data/Act_loss', act_losses.val, epoch*len(train_loader)+i+1)
        writer.add_scalar('data/comp_loss', comp_losses.val, epoch*len(train_loader)+i+1)

        if i % args.print_freq == 0:
            logger.info('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Act. Loss {act_losses.val:.3f} ({act_losses.avg: .3f}) \t'
                  'Comp. Loss {comp_losses.val:.3f} ({comp_losses.avg: .3f}) '
                  .format(
                   epoch, i, len(train_loader), batch_time=batch_time,
                   data_time=data_time, loss=losses, act_losses=act_losses,
                    comp_losses=comp_losses, lr=optimizer.param_groups[0]['lr'], ) +
                  '\tReg. Loss {reg_loss.val:.3f} ({reg_loss.avg:.3f})'.format(
                      reg_loss=reg_losses)
                  + '\n Act. FG {fg_acc.val:.02f} ({fg_acc.avg:.02f}) Act. BG {bg_acc.avg:.02f} ({bg_acc.avg:.02f})'
                  .format(act_acc=act_accuracies,
                    fg_acc=fg_accuracies, bg_acc=bg_accuracies)
                  )


def validate(val_loader, model, act_criterion, comp_criterion, regression_criterion, iter):
    batch_time = AverageMeter()
    losses = AverageMeter()
    act_losses = AverageMeter()
    comp_losses = AverageMeter()
    reg_losses = AverageMeter()
    act_accuracies = AverageMeter()
    fg_accuracies = AverageMeter()
    bg_accuracies = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()

    ohem_num = val_loader.dataset.fg_per_video
    comp_group_size = val_loader.dataset.fg_per_video + val_loader.dataset.incomplete_per_video

    for i, (batch_act_fts, batch_comp_fts, batch_props_type,
            batch_props_label, batch_reg_target, props_num_list, vid_list) in enumerate(val_loader):
        # measure data loading time
        batch_size = batch_act_fts.size(0)

        activity_out, activity_target, activity_prop_type, \
        completeness_out, completeness_target, \
        regression_out, regression_labels, regression_target, ohem_num = model(batch_act_fts, batch_comp_fts, batch_props_type,
                                                                     batch_props_label, batch_reg_target, props_num_list,
                                                                     vid_list, False)

        act_loss = act_criterion(activity_out, activity_target)
        comp_loss = comp_criterion(completeness_out, completeness_target, ohem_num, 7 * ohem_num)
        reg_loss = regression_criterion(regression_out, regression_labels, regression_target)

        loss = act_loss + comp_loss * args.comp_loss_weight + reg_loss * args.reg_loss_weight

        losses.update(loss.item(), batch_size)
        act_losses.update(act_loss.item(), batch_size)
        comp_losses.update(comp_loss.item(), batch_size)
        reg_losses.update(reg_loss.item(), batch_size)

        act_acc = accuracy(activity_out, activity_target)
        act_accuracies.update(act_acc[0].item(), activity_out.size(0))

        fg_indexer = (activity_prop_type == 0).nonzero().squeeze()
        bg_indexer = (activity_prop_type == 2).nonzero().squeeze()

        fg_acc = accuracy(activity_out[fg_indexer], activity_target[fg_indexer])
        fg_accuracies.update(fg_acc[0].item(), len(fg_indexer))

        if len(bg_indexer) > 0:
            bg_acc = accuracy(activity_out[bg_indexer], activity_target[bg_indexer])
            bg_accuracies.update(bg_acc[0].item(), len(bg_indexer))

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

        if i % args.print_freq == 0:
            logger.info('Test: [{0}/{1}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Act. Loss {act_loss.val:.3f} ({act_loss.avg:.3f})\t'
                  'Comp. Loss {comp_loss.val:.3f} ({comp_loss.avg:.3f})\t'
                  'Act. Accuracy {act_acc.val:.02f} ({act_acc.avg:.2f}) FG {fg_acc.val:.02f} BG {bg_acc.val:.02f}'.format(
                   i, len(val_loader), batch_time=batch_time, loss=losses,
                    act_loss=act_losses, comp_loss=comp_losses, act_acc=act_accuracies,
                    fg_acc=fg_accuracies, bg_acc=bg_accuracies) +
                  '\tReg. Loss {reg_loss.val:.3f} ({reg_loss.avg:.3f})'.format(
                      reg_loss=reg_losses))

    logger.info('Testing Results: Loss {loss.avg:.5f} \t '
          'Activity Loss {act_loss.avg:.3f} \t '
          'Completeness Loss {comp_loss.avg:.3f}\n'
          'Act Accuracy {act_acc.avg:.02f} FG Acc. {fg_acc.avg:.02f} BG Acc. {bg_acc.avg:.02f}'
          .format(act_loss=act_losses, comp_loss=comp_losses, loss=losses, act_acc=act_accuracies,
                  fg_acc=fg_accuracies, bg_acc=bg_accuracies)
          + '\t Regression Loss {reg_loss.avg:.3f}'.format(reg_loss=reg_losses))

    return losses.avg


def save_checkpoint(state, is_best, epoch, filename='checkpoint.pth.tar'):
    filename = args.snapshot_pref + '_'.join(('PGCN', args.dataset, 'epoch', str(epoch), filename))
    torch.save(state, filename)
    if is_best:
        best_name = args.snapshot_pref + '_'.join(('PGCN', args.dataset, 'model_best.pth.tar'))
        shutil.copyfile(filename, best_name)

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def adjust_learning_rate(optimizer, epoch, lr_steps):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    decay = 0.1 ** (sum(epoch >= np.array(lr_steps)))
    lr = args.lr * decay
    decay = args.weight_decay
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr * param_group['lr_mult']
        param_group['weight_decay'] = decay * param_group['decay_mult']


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()