295 lines
12 KiB
Python
295 lines
12 KiB
Python
from __future__ import print_function, 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.optim import lr_scheduler
|
|
|
|
import data_manager
|
|
from dataset_loader import ImageDataset
|
|
import transforms as T
|
|
import models
|
|
from losses import CrossEntropyLabelSmooth, TripletLoss, DeepSupervision
|
|
from utils import AverageMeter, Logger, save_checkpoint
|
|
from eval_metrics import evaluate
|
|
from samplers import RandomIdentitySampler
|
|
from optimizers import init_optim
|
|
|
|
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")
|
|
# CUHK03-specific setting
|
|
parser.add_argument('--cuhk03-labeled', action='store_true',
|
|
help="whether to use labeled images, if false, detected images are used (default: False)")
|
|
parser.add_argument('--cuhk03-classic-split', action='store_true',
|
|
help="whether to use classic split by Li et al. CVPR'14 (default: False)")
|
|
parser.add_argument('--use-metric-cuhk03', action='store_true',
|
|
help="whether to 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=180, 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=60, 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)")
|
|
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', default=False,
|
|
help="if this is True, only htri loss is used in training")
|
|
# 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 {}".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_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.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),
|
|
sampler=RandomIdentitySampler(dataset.train, num_instances=args.num_instances),
|
|
batch_size=args.train_batch, 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', 'htri'})
|
|
print("Model size: {:.5f}M".format(sum(p.numel() for p in model.parameters())/1000000.0))
|
|
|
|
criterion_xent = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
|
|
criterion_htri = TripletLoss(margin=args.margin)
|
|
|
|
optimizer = init_optim(args.optim, model.parameters(), args.lr, 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()
|
|
train_time = 0
|
|
best_rank1 = -np.inf
|
|
best_epoch = 0
|
|
print("==> Start training")
|
|
|
|
for epoch in range(start_epoch, args.max_epoch):
|
|
start_train_time = time.time()
|
|
train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu)
|
|
train_time += round(time.time() - start_train_time)
|
|
|
|
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
|
|
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))
|
|
|
|
def train(epoch, model, criterion_xent, criterion_htri, 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()
|
|
outputs, features = model(imgs)
|
|
if args.htri_only:
|
|
if isinstance(features, tuple):
|
|
loss = DeepSupervision(criterion_htri, features, pids)
|
|
else:
|
|
loss = criterion_htri(features, pids)
|
|
else:
|
|
if isinstance(outputs, tuple):
|
|
xent_loss = DeepSupervision(criterion_xent, outputs, pids)
|
|
else:
|
|
xent_loss = criterion_xent(outputs, pids)
|
|
|
|
if isinstance(features, tuple):
|
|
htri_loss = DeepSupervision(criterion_htri, features, pids)
|
|
else:
|
|
htri_loss = criterion_htri(features, pids)
|
|
|
|
loss = xent_loss + htri_loss
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
losses.update(loss.item(), pids.size(0))
|
|
|
|
if (batch_idx+1) % args.print_freq == 0:
|
|
print("Epoch {}/{}\t Batch {}/{}\t Loss {:.6f} ({:.6f})".format(
|
|
epoch+1, args.max_epoch, batch_idx+1, len(trainloader), losses.val, losses.avg
|
|
))
|
|
|
|
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
|
|
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()
|
|
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()
|
|
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, 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("------------------")
|
|
|
|
return cmc[0]
|
|
|
|
if __name__ == '__main__':
|
|
main() |