train_vidreid_xent.py

from __future__ import print_function
from __future__ import division

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

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

from args import argument_parser, video_dataset_kwargs, optimizer_kwargs, lr_scheduler_kwargs
from torchreid.data_manager import VideoDataManager
from torchreid import models
from torchreid.losses import CrossEntropyLoss
from torchreid.utils.iotools import check_isfile
from torchreid.utils.avgmeter import AverageMeter
from torchreid.utils.loggers import Logger, RankLogger
from torchreid.utils.torchtools import count_num_param, open_all_layers, open_specified_layers, accuracy, \
    load_pretrained_weights, save_checkpoint, resume_from_checkpoint
from torchreid.utils.reidtools import visualize_ranked_results
from torchreid.utils.generaltools import set_random_seed
from torchreid.eval_metrics import evaluate
from torchreid.optimizers import init_optimizer
from torchreid.lr_schedulers import init_lr_scheduler


# global variables
parser = argument_parser()
args = parser.parse_args()


def main():
    global args
    
    set_random_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
    log_name = 'log_test.txt' if args.evaluate else 'log_train.txt'
    sys.stdout = Logger(osp.join(args.save_dir, log_name))
    print('==========\nArgs:{}\n=========='.format(args))

    if use_gpu:
        print('Currently using GPU {}'.format(args.gpu_devices))
        cudnn.benchmark = True
    else:
        warnings.warn('Currently using CPU, however, GPU is highly recommended')

    print('Initializing video data manager')
    dm = VideoDataManager(use_gpu, **video_dataset_kwargs(args))
    trainloader, testloader_dict = dm.return_dataloaders()

    print('Initializing model: {}'.format(args.arch))
    model = models.init_model(name=args.arch, num_classes=dm.num_train_pids, loss={'xent'}, pretrained=not args.no_pretrained, use_gpu=use_gpu)
    print('Model size: {:.3f} M'.format(count_num_param(model)))

    if args.load_weights and check_isfile(args.load_weights):
        load_pretrained_weights(model, args.load_weights)

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

    criterion = CrossEntropyLoss(num_classes=dm.num_train_pids, use_gpu=use_gpu, label_smooth=args.label_smooth)
    optimizer = init_optimizer(model, **optimizer_kwargs(args))
    scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs(args))

    if args.resume and check_isfile(args.resume):
        args.start_epoch = resume_from_checkpoint(args.resume, model, optimizer=optimizer)

    if args.evaluate:
        print('Evaluate only')

        for name in args.target_names:
            print('Evaluating {} ...'.format(name))
            queryloader = testloader_dict[name]['query']
            galleryloader = testloader_dict[name]['gallery']
            distmat = test(model, queryloader, galleryloader, args.pool_tracklet_features, use_gpu, return_distmat=True)
        
            if args.visualize_ranks:
                visualize_ranked_results(
                    distmat, dm.return_testdataset_by_name(name),
                    save_dir=osp.join(args.save_dir, 'ranked_results', name),
                    topk=20
                )
        return

    time_start = time.time()
    ranklogger = RankLogger(args.source_names, args.target_names)
    print('=> Start training')

    if args.fixbase_epoch > 0:
        print('Train {} for {} epochs while keeping other layers frozen'.format(args.open_layers, args.fixbase_epoch))
        initial_optim_state = optimizer.state_dict()

        for epoch in range(args.fixbase_epoch):
            train(epoch, model, criterion, optimizer, trainloader, use_gpu, fixbase=True)

        print('Done. All layers are open to train for {} epochs'.format(args.max_epoch))
        optimizer.load_state_dict(initial_optim_state)

    for epoch in range(args.start_epoch, args.max_epoch):
        train(epoch, model, criterion, optimizer, trainloader, use_gpu)
        
        scheduler.step()
        
        if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (epoch + 1) == args.max_epoch:
            print('=> Test')
            
            for name in args.target_names:
                print('Evaluating {} ...'.format(name))
                queryloader = testloader_dict[name]['query']
                galleryloader = testloader_dict[name]['gallery']
                rank1 = test(model, queryloader, galleryloader, args.pool_tracklet_features, use_gpu)
                ranklogger.write(name, epoch + 1, rank1)
            
            save_checkpoint({
                'state_dict': model.state_dict(),
                'rank1': rank1,
                'epoch': epoch + 1,
                'arch': args.arch,
                'optimizer': optimizer.state_dict(),
            }, args.save_dir)

    elapsed = round(time.time() - time_start)
    elapsed = str(datetime.timedelta(seconds=elapsed))
    print('Elapsed {}'.format(elapsed))
    ranklogger.show_summary()


def train(epoch, model, criterion, optimizer, trainloader, use_gpu, fixbase=False):
    losses = AverageMeter()
    accs = AverageMeter()
    batch_time = AverageMeter()
    data_time = AverageMeter()

    model.train()

    if fixbase or args.always_fixbase:
        open_specified_layers(model, args.open_layers)
    else:
        open_all_layers(model)

    end = time.time()
    for batch_idx, (imgs, pids, _, _) in enumerate(trainloader):
        data_time.update(time.time() - end)
        
        if use_gpu:
            imgs, pids = imgs.cuda(), pids.cuda()
        
        outputs = model(imgs)
        if isinstance(outputs, (tuple, list)):
            loss = DeepSupervision(criterion, outputs, pids)
        else:
            loss = criterion(outputs, pids)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        losses.update(loss.item(), pids.size(0))
        accs.update(accuracy(outputs, pids)[0])

        if (batch_idx + 1) % args.print_freq == 0:
            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'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Acc {acc.val:.2f} ({acc.avg:.2f})\t'.format(
                   epoch + 1, batch_idx + 1, len(trainloader),
                   batch_time=batch_time,
                   data_time=data_time,
                   loss=losses,
                   acc=accs
            ))
        
        end = time.time()


def test(model, queryloader, galleryloader, pool, use_gpu, 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()
            b, s, c, h, w = imgs.size()
            imgs = imgs.view(b*s, c, h, w)

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

            features = features.view(b, s, -1)
            if pool == 'avg':
                features = torch.mean(features, 1)
            else:
                features, _ = torch.max(features, 1)
            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()
            b, s, c, h, w = imgs.size()
            imgs = imgs.view(b*s, c, h, w)
            
            end = time.time()
            features = model(imgs)
            batch_time.update(time.time() - end)

            features = features.view(b, s, -1)
            if pool == 'avg':
                features = torch.mean(features, 1)
            else:
                features, _ = torch.max(features, 1)
            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_size * args.seq_len))

    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)

    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
    return cmc[0]


if __name__ == '__main__':
    main()