from __future__ import absolute_import 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.autograd import Variable from torch.optim import lr_scheduler import data_manager from dataset_loader import ImageDataset import transforms as T import models from losses import CrossEntropyLabelSmooth from utils import AverageMeter, Logger, save_checkpoint from eval_metrics import evaluate parser = argparse.ArgumentParser(description='Train image model with cross entropy loss') # Datasets 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)") # Optimization options 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=32, 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, type=int, help="stepsize to decay learning rate (>0 means this is enabled)") 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)") # 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('--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('--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') args = parser.parse_args() def main(): torch.manual_seed(args.seed) 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") 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_dataset(name=args.dataset) transform_train = T.Compose([ T.Random2DTranslation(args.height, args.width), T.RandomHorizontalFlip(), 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(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), 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), 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'}) print("Model size: {:.5f}M".format(sum(p.numel() for p in model.parameters())/1000000.0)) criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) if args.stepsize > 0: scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma) start_epoch = args.start_epoch if args.resume: print("Loading checkpoint from '{}'".format(args.resume)) checkpoint = torch.load(args.resume) model.load_state_dict(checkpoint['state_dict']) start_epoch = checkpoint['epoch'] if use_gpu: model = nn.DataParallel(model).cuda() if args.evaluate: print("Evaluate only") test(model, queryloader, galleryloader, use_gpu) return start_time = time.time() best_rank1 = -np.inf for epoch in range(start_epoch, args.max_epoch): print("==> Epoch {}/{}".format(epoch+1, args.max_epoch)) train(model, criterion, optimizer, trainloader, use_gpu) if args.stepsize > 0: scheduler.step() if 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) is_best = rank1 > best_rank1 if is_best: best_rank1 = rank1 save_checkpoint({ 'state_dict': model.state_dict(), 'rank1': rank1, 'epoch': epoch, }, is_best, osp.join(args.save_dir, 'checkpoint_ep' + str(epoch+1) + '.pth.tar')) elapsed = round(time.time() - start_time) elapsed = str(datetime.timedelta(seconds=elapsed)) print("Finished. Total elapsed time (h:m:s): {}".format(elapsed)) def train(model, criterion, optimizer, trainloader, use_gpu): model.train() losses = AverageMeter() for batch_idx, (imgs, pids, _) in enumerate(trainloader): if use_gpu: imgs, pids = imgs.cuda(), pids.cuda() imgs, pids = Variable(imgs), Variable(pids) outputs = model(imgs) loss = criterion(outputs, pids) optimizer.zero_grad() loss.backward() optimizer.step() losses.update(loss.data[0], pids.size(0)) if (batch_idx+1) % args.print_freq == 0: print("Batch {}/{}\t Loss {:.6f} ({:.6f})".format(batch_idx+1, len(trainloader), losses.val, losses.avg)) def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]): model.eval() qf, q_pids, q_camids = [], [], [] for batch_idx, (imgs, pids, camids) in enumerate(queryloader): if use_gpu: imgs = imgs.cuda() imgs = Variable(imgs) features = model(imgs) 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() imgs = Variable(imgs) features = model(imgs) 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("Computing distance matrix") 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("------------------") return cmc[0] if __name__ == '__main__': main()