train_imgreid_xent_customSampling.py

from __future__ import print_function
from __future__ import division

import os
import sys
import time
import datetime
import argparse
import os.path as osp
import numpy as np

import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.optim import lr_scheduler

from torchreid import data_manager
from torchreid.dataset_loader_custom import ImageDataset_customSampling, ImageDataset
from torchreid import transforms as T
from torchreid import models
from torchreid.losses import CrossEntropyLabelSmooth, TripletLoss_custom, DeepSupervision,SoftTripletLoss_custom,ConfidencePenalty
from torchreid.utils.iotools import save_checkpoint, check_isfile
from torchreid.utils.avgmeter import AverageMeter
from torchreid.utils.logger import Logger
from torchreid.utils.torchtools import set_bn_to_eval, count_num_param
from torchreid.utils.reidtools import visualize_ranked_results, drawTSNE
from torchreid.eval_metrics import evaluate
from torchreid.samplers import RandomIdentitySampler
from torchreid.optimizers import init_optim

from tensorboardX import SummaryWriter
parser = argparse.ArgumentParser(description='Train image model with cross entropy loss and hard triplet loss')
# Datasets
parser.add_argument('--root', type=str, default='data',
                    help="root path to data directory")
parser.add_argument('-d', '--dataset', type=str, default='market1501',
                    choices=data_manager.get_names())
parser.add_argument('-j', '--workers', default=4, type=int,
                    help="number of data loading workers (default: 4)")
parser.add_argument('--height', type=int, default=256,
                    help="height of an image (default: 256)")
parser.add_argument('--width', type=int, default=128,
                    help="width of an image (default: 128)")
parser.add_argument('--split-id', type=int, default=0,
                    help="split index (0-based)")
# CUHK03-specific setting
parser.add_argument('--cuhk03-labeled', action='store_true',
                    help="use labeled images, if false, detected images are used (default: False)")
parser.add_argument('--cuhk03-classic-split', action='store_true',
                    help="use classic split by Li et al. CVPR'14 (default: False)")
parser.add_argument('--use-metric-cuhk03', action='store_true',
                    help="use cuhk03-metric (default: False)")
# Optimization options
parser.add_argument('--optim', type=str, default='adam',
                    help="optimization algorithm (see optimizers.py)")
parser.add_argument('--max-epoch', default=60, type=int,
                    help="maximum epochs to run")
parser.add_argument('--start-epoch', default=0, type=int,
                    help="manual epoch number (useful on restarts)")
parser.add_argument('--train-batch', default=32, type=int,
                    help="train batch size")
parser.add_argument('--test-batch', default=100, type=int,
                    help="test batch size")
parser.add_argument('--lr', '--learning-rate', default=0.0003, type=float,
                    help="initial learning rate")
parser.add_argument('--stepsize', default=[20, 40], nargs='+', type=int,
                    help="stepsize to decay learning rate")
parser.add_argument('--gamma', default=0.1, type=float,
                    help="learning rate decay")
parser.add_argument('--weight-decay', default=5e-04, type=float,
                    help="weight decay (default: 5e-04)")
parser.add_argument('--margin', type=float, default=0.3,
                    help="margin for triplet loss")
parser.add_argument('--num-instances', type=int, default=4,
                    help="number of instances per identity")
parser.add_argument('--htri-only', action='store_true',
                    help="only use hard triplet loss (default: Fasle)")
parser.add_argument('--lambda-xent', type=float, default=1,
                    help="weight to balance cross entropy loss")
parser.add_argument('--lambda-htri', type=float, default=1,
                    help="weight to balance hard triplet loss")
parser.add_argument('--label-smooth', action='store_true',
                    help="use label smoothing regularizer in cross entropy loss")

parser.add_argument('--fixbase-epoch', default=0, type=int,
                    help="epochs to fix base network (only train classifier, default: 0)")
parser.add_argument('--fixbase-lr', default=0.0003, type=float,
                    help="learning rate (when base network is frozen)")
parser.add_argument('--freeze-bn', action='store_true',
                    help="freeze running statistics in BatchNorm layers during training (default: False)")
# Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
# Miscs
parser.add_argument('--print-freq', type=int, default=10,
                    help="print frequency")
parser.add_argument('--seed', type=int, default=1,
                    help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument('--load-weights', type=str, default='',
                    help="load pretrained weights but ignores layers that don't match in size")
parser.add_argument('--evaluate', action='store_true',
                    help="evaluation only")
parser.add_argument('--eval-step', type=int, default=-1,
                    help="run evaluation for every N epochs (set to -1 to test after training)")
parser.add_argument('--start-eval', type=int, default=0,
                    help="start to evaluate after specific epoch")
parser.add_argument('--save-dir', type=str, default='log')
parser.add_argument('--use-cpu', action='store_true',
                    help="use cpu")
parser.add_argument('--gpu-devices', default='0', type=str,
                    help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--use-avai-gpus', action='store_true',
                    help="use available gpus instead of specified devices (this is useful when using managed clusters)")
parser.add_argument('--visualize-ranks', action='store_true',
                    help="visualize ranked results, only available in evaluation mode (default: False)")

parser.add_argument('--soft-margin', action='store_true',
                    help="use Soft triplet loss (default: Fasle)")
# global variables
parser.add_argument('--draw-tsne', action='store_true',
                    help="Plot TSNE Clusters (default: False)")
parser.add_argument('--tsne-labels',type=int, default=3,
                    help="Number of TSNE Clusters (Default: 3)")

parser.add_argument('--confidence-penalty', action='store_true',
                    help="use confidence penalty regularizer in cross entropy loss")
parser.add_argument('--confidence-beta',type=float, default=1,
                    help="set confidence penalty beta ")

def main(args):
    args = parser.parse_args(args)
    best_rank1 = -np.inf
    torch.manual_seed(args.seed)
    if not args.use_avai_gpus: os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
    use_gpu = torch.cuda.is_available()
    if args.use_cpu: use_gpu = False

    if not args.evaluate:
        sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
    else:
        sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
    print("==========\nArgs:{}\n==========".format(args))

    if use_gpu:
        print("Currently using GPU {}".format(args.gpu_devices))
        cudnn.benchmark = True
        torch.cuda.manual_seed_all(args.seed)
    else:
        print("Currently using CPU (GPU is highly recommended)")

    print("Initializing dataset {}".format(args.dataset))
    dataset = data_manager.init_imgreid_dataset(
        root=args.root, name=args.dataset, split_id=args.split_id,
        cuhk03_labeled=args.cuhk03_labeled, cuhk03_classic_split=args.cuhk03_classic_split,
    )

    transform_train = T.Compose([
        #T.Random2DTranslation(args.height, args.width),
        T.Resize((args.height, args.width)),
        T.RandomSizedEarser(),
        T.RandomHorizontalFlip_custom(),
        #T.Resize((args.height, args.width)),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    transform_test = T.Compose([
        T.Resize((args.height, args.width)),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    pin_memory = True if use_gpu else False

    trainloader = DataLoader(
        ImageDataset_customSampling(dataset.train, transform=transform_train),
        batch_size=args.train_batch, shuffle=True, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=True,
    )

    queryloader = DataLoader(
        ImageDataset(dataset.query, transform=transform_test, return_path=args.draw_tsne),
        batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=False,
    )

    galleryloader = DataLoader(
        ImageDataset(dataset.gallery, transform=transform_test, return_path=args.draw_tsne),
        batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=False,
    )

    print("Initializing model: {}".format(args.arch))
    model = models.init_model(name=args.arch, num_classes=dataset.num_train_pids, loss={'xent', 'htri'})
    print("Model size: {:.3f} M".format(count_num_param(model)))

    if args.label_smooth:
        criterion_xent = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
    else:
        criterion_xent = nn.CrossEntropyLoss()

    if args.soft_margin:
        criterion_htri = SoftTripletLoss_custom()
    else:
        criterion_htri = TripletLoss_custom(margin=args.margin)

    criterion_xent = (criterion_xent,ConfidencePenalty())
    optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)
    if args.fixbase_epoch > 0:
        if hasattr(model, 'classifier') and isinstance(model.classifier, nn.Module):
            optimizer_tmp = init_optim(args.optim, model.classifier.parameters(), args.fixbase_lr, args.weight_decay)
        else:
            print("Warn: model has no attribute 'classifier' and fixbase_epoch is reset to 0")
            args.fixbase_epoch = 0
    if args.load_weights and check_isfile(args.load_weights):
        # load pretrained weights but ignore layers that don't match in size
        checkpoint = torch.load(args.load_weights)
        pretrain_dict = checkpoint['state_dict']
        model_dict = model.state_dict()
        pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
        model_dict.update(pretrain_dict)
        model.load_state_dict(model_dict)
        print("Loaded pretrained weights from '{}'".format(args.load_weights))

    if args.resume and check_isfile(args.resume):
        checkpoint = torch.load(args.resume)
        model.load_state_dict(checkpoint['state_dict'])
        args.start_epoch = checkpoint['epoch'] + 1
        best_rank1 = checkpoint['rank1']
        print("Loaded checkpoint from '{}'".format(args.resume))
        print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch, best_rank1))

    if use_gpu:
        model = nn.DataParallel(model).cuda()

    if args.evaluate:
        print("Evaluate only")
        distmat = test(model, queryloader, galleryloader, use_gpu,args,writer=None,epoch=-1, return_distmat=True)
        if args.visualize_ranks:
            visualize_ranked_results(
                distmat, dataset,
                save_dir=osp.join(args.save_dir, 'ranked_results'),
                topk=20,
            )
        return

    writer = SummaryWriter(log_dir=osp.join(args.save_dir, 'tensorboard'))
    start_time = time.time()
    train_time = 0
    best_epoch = args.start_epoch
    print("==> Start training")

    if args.fixbase_epoch > 0:
        print("Train classifier for {} epochs while keeping base network frozen".format(args.fixbase_epoch))

        for epoch in range(args.fixbase_epoch):
            start_train_time = time.time()
            train(epoch, model, criterion_xent, None, optimizer_tmp, trainloader, use_gpu,writer, args, freeze_bn=True)
            train_time += round(time.time() - start_train_time)

        del optimizer_tmp
        print("Now open all layers for training")

    for epoch in range(args.start_epoch, args.max_epoch):
        start_train_time = time.time()
        train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu,writer, args)
        train_time += round(time.time() - start_train_time)

        scheduler.step()

        if (epoch + 1) > args.start_eval and args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (epoch + 1) == args.max_epoch:
            print("==> Test")
            rank1 = test(model, queryloader, galleryloader, use_gpu,args=args,writer=writer,epoch=epoch)
            is_best = rank1 > best_rank1

            if is_best:
                best_rank1 = rank1
                best_epoch = epoch + 1

            if use_gpu:
                state_dict = model.module.state_dict()
            else:
                state_dict = model.state_dict()

            save_checkpoint({
                'state_dict': state_dict,
                'rank1': rank1,
                'epoch': epoch,
            }, is_best, osp.join(args.save_dir, 'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))

    print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(best_rank1, best_epoch))

    elapsed = round(time.time() - start_time)
    elapsed = str(datetime.timedelta(seconds=elapsed))
    train_time = str(datetime.timedelta(seconds=train_time))
    print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
    return best_rank1, best_epoch

def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu,writer, args, freeze_bn=False):
    losses = AverageMeter()
    xent_losses = AverageMeter()
    confidence_losses = AverageMeter()
    htri_losses = AverageMeter()
    batch_time = AverageMeter()
    data_time = AverageMeter()
    printed = False
    model.train()
    if freeze_bn or args.freeze_bn:
        model.apply(set_bn_to_eval)
    end = time.time()
    for batch_idx, ((imgs_a, pids_a, _),(imgs_p,pids_p,_)) in enumerate(trainloader):
        data_time.update(time.time() - end)

        if use_gpu:
            imgs_a, pids_a = imgs_a.cuda(), pids_a.cuda()
            imgs_p, pids_p = imgs_p.cuda(), pids_p.cuda()

        outputs_a,features_a = model(imgs_a)
        _, features_p = model(imgs_p)
        if isinstance(outputs_a, tuple):
            xent_loss = DeepSupervision(criterion_xent[0], outputs_a, pids_a)
        else:
            xent_loss = criterion_xent[0](outputs_a, pids_a)

        confidence_loss = criterion_xent[1](outputs_a)
        htri_loss = 0
        if not (criterion_htri is None):
            htri_loss = criterion_htri(features_a,features_p, pids_a,pids_p)

        if args.confidence_penalty:
            loss = args.lambda_xent * xent_loss + args.lambda_htri * htri_loss - args.confidence_beta *confidence_loss
        else:
            loss = args.lambda_xent * xent_loss + args.lambda_htri * htri_loss
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        losses.update(loss.item(), pids_a.size(0))
        confidence_losses.update(confidence_loss.item(), pids_a.size(0))
        if not args.htri_only:
            xent_losses.update(xent_loss.item(), pids_a.size(0))
        if criterion_htri is None:
            htri_losses.update(htri_loss, pids_a.size(0))
        else:
            htri_losses.update(htri_loss.item(), pids_a.size(0))
        if (batch_idx + 1) % args.print_freq == 0:
            if not printed:
              printed = True
            else:
              # Clean the current line
              sys.stdout.console.write("\033[F\033[K")
              #sys.stdout.console.write("\033[K")
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.4f} ({data_time.avg:.4f})\t'
                  'Xent Loss {xent_loss.val:.4f} ({xent_loss.avg:.4f})\t'
                  'Confi_Loss {confidence_loss.val:.4f} ({confidence_loss.avg:.4f})\t'
                  'Htri Loss {htri_loss.val:.4f} ({htri_loss.avg:.4f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
                   epoch + 1, batch_idx + 1, len(trainloader), batch_time=batch_time,
                   data_time=data_time,xent_loss=xent_losses,confidence_loss=confidence_losses,htri_loss=htri_losses, loss=losses))

        end = time.time()

    writer.add_scalars(
      'Losses',
      dict(total_loss=losses.avg,
           xen_loss=xent_losses.avg,
           htri_loss=htri_losses.avg,
           confidence_loss = confidence_losses.avg),
      epoch + 1)


def test(model, queryloader, galleryloader, use_gpu,args,writer,epoch, ranks=[1, 5, 10, 20], return_distmat=False):
    batch_time = AverageMeter()

    model.eval()

    with torch.no_grad():
        qf, q_pids, q_camids = [], [], []
        for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
            if use_gpu:
                imgs = imgs.cuda()

            end = time.time()
            features = model(imgs)
            batch_time.update(time.time() - end)

            features = features.data.cpu()
            qf.append(features)
            q_pids.extend(pids)
            q_camids.extend(camids)
        qf = torch.cat(qf, 0)
        q_pids = np.asarray(q_pids)
        q_camids = np.asarray(q_camids)

        print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))

        gf, g_pids, g_camids = [], [], []
        for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
            if use_gpu:
                imgs = imgs.cuda()

            end = time.time()
            features = model(imgs)
            batch_time.update(time.time() - end)

            features = features.data.cpu()
            gf.append(features)
            g_pids.extend(pids)
            g_camids.extend(camids)
        gf = torch.cat(gf, 0)
        g_pids = np.asarray(g_pids)
        g_camids = np.asarray(g_camids)

        print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))

    print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch))

    m, n = qf.size(0), gf.size(0)
    distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
              torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
    distmat.addmm_(1, -2, qf, gf.t())
    distmat = distmat.numpy()

    print("Computing CMC and mAP")
    cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=args.use_metric_cuhk03)

    print("Results ----------")
    print("mAP: {:.1%}".format(mAP))
    print("CMC curve")
    for r in ranks:
        print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1]))
    print("------------------")

    if return_distmat:
        return distmat
    if writer != None:
        writer.add_scalars(
          'Testing',
          dict(rank_1=cmc[0],
               rank_5 = cmc[4],
               mAP=mAP),
          epoch + 1)
    return cmc[0]


if __name__ == '__main__':
    main(sys.argv[1:])