def set_transform():
if args.is_REA:
transform_train = T.Compose([
T.Random2DTranslation(args.height, args.width),
T.RandomHorizontalFlip(),
T.RandomEraising(),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
else:
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])
])
return transform_train,transform_test
def chart_recognition(model_chart, img_dir):
# img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
transform = T.Compose([
T.Resize((256, 128)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
loader = DataLoader(
ImageDataset_demo(img_dir, transform=transform),
batch_size=1,
shuffle=False,
num_workers=0,
pin_memory=True,
drop_last=False,
)
model_chart.eval()
with torch.no_grad():
for batch_idx, img2 in enumerate(loader):
if torch.cuda.is_available(): img2 = img2.cuda()
score = model_chart(img2)
print(score)
chart = torch.argmax(score.data, 1)
chart = chart[0].cpu().numpy()
print(chart)
kind = num2label[str(chart)]
return kind
def main():
transform_test = T.Compose([
T.Resize((256, 128)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
use_gpu = torch.cuda.is_available()
model = models.init_model(name=args.arch, num_classes=751, loss={'xent'})
checkpoint = torch.load(os.path.join('./model', 'best_model.pth.tar'))
model.load_state_dict(checkpoint['state_dict'])
model.classifier = nn.Sequential()
if use_gpu:
model = nn.DataParallel(model).cuda()
model.eval()
for dataset in ['val', 'test']:
for subset in ['query', 'gallery']:
test_names, test_features = extractor(
model,
DataLoader(
Dataset(dataset + '/' + subset, transform=transform_test)))
results = {'names': test_names, 'features': test_features.numpy()}
scipy.io.savemat(
os.path.join('log_dir',
'feature_%s_%s.mat' % (dataset, subset)), results)
def main():
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.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'},
use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
if args.label_smooth:
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
else:
criterion = nn.CrossEntropyLoss()
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,
return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
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,
optimizer_tmp,
trainloader,
use_gpu,
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, optimizer, trainloader, use_gpu)
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)
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 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
logger_info = LoggerInfo()
sys.stdout = Logger(logger_info)
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.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
train_dataset = ImageFolder(os.path.join(args.data_dir, "train_all"),
transform=transform_train)
train_query_dataset = ImageFolder(os.path.join(args.data_dir, "val"),
transform=transform_train)
train_gallery_dataset = ImageFolder(os.path.join(args.data_dir, "train"),
transform=transform_train)
test_dataset = ImageFolder(os.path.join(args.data_dir, "probe"),
transform=transform_test)
query_dataset = ImageFolder(os.path.join(args.data_dir, "query"),
transform=transform_test)
gallery_dataset = ImageFolder(os.path.join(args.data_dir, "gallery"),
transform=transform_test)
# train_batch_sampler = VehicleIdBalancedBatchSampler(train_dataset, n_classes=8, n_samples=6)
# test_batch_sampler = VehicleIdBalancedBatchSampler(test_dataset, n_classes=8, n_samples=8)
train_batch_sampler = VehicleIdCCLBatchSampler(train_dataset,
n_classes=n_cls,
n_samples=n_samples)
trainloader = DataLoader(train_dataset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=pin_memory)
testloader = DataLoader(test_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False)
# trainloader = DataLoader(
# ImageDataset(dataset.train, transform=transform_train),
# batch_sampler=train_batch_sampler, batch_size=args.train_batch,
# shuffle=True, num_workers=args.workers, pin_memory=pin_memory, drop_last=True
# )
queryloader = DataLoader(
query_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
gallery_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
train_query_loader = DataLoader(
train_query_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
train_gallery_loader = DataLoader(
train_gallery_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
print("Initializing model: {}".format(args.arch))
if args.evaluate:
model = models.init_model(name=args.arch,
num_classes=len(query_dataset.classes),
loss_type=args.loss_type)
else:
model = models.init_model(name=args.arch,
num_classes=len(train_dataset.classes),
loss_type=args.loss_type)
print("Model size: {:.3f} M".format(count_num_param(model)))
if args.label_smooth:
criterion = CrossEntropyLabelSmooth(num_classes=len(
train_dataset.classes),
use_gpu=use_gpu)
else:
if args.loss_type == 'xent':
criterion = nn.CrossEntropyLoss()
elif args.loss_type == 'angle':
criterion = AngleLoss()
elif args.loss_type == 'triplet':
# criterion = CoupledClustersLoss(margin=1., triplet_selector=RandomNegativeTripletSelector(margin=1.))
# criterion = OnlineTripletLoss(margin=1., triplet_selector=RandomNegativeTripletSelector(margin=1.))
# criterion = OnlineTripletLoss(margin=1., triplet_selector=HardestNegativeTripletSelector(margin=1.))
criterion = CoupledClustersLoss(margin=1.,
n_classes=n_cls,
n_samples=n_samples)
# criterion = OnlineTripletLoss(margin=1., triplet_selector=SemihardNegativeTripletSelector(margin=1.))
elif args.loss_type == 'xent_htri':
criterion = XentTripletLoss(
margin=1.,
triplet_selector=RandomNegativeTripletSelector(margin=1.))
else:
raise KeyError("Unsupported loss: {}".format(args.loss_type))
# model_param_list = [{'params': model.base.parameters(), 'lr': args.lr},
# {'params': model.classifier.parameters(), 'lr': args.lr * 10}]
# optimizer = init_optim(args.optim, model_param_list, lr=1.0, weight_decay=args.weight_decay)
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:
# load pretrained weights but ignore layers that don't match in size
if check_isfile(args.load_weights):
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:
from functools import partial
import pickle
pickle.load = partial(pickle.load, encoding="latin1")
pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
if check_isfile(args.resume):
checkpoint = torch.load(args.resume)
# checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage, pickle_module=pickle)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch'] + 1
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(
args.start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
# if args.evaluate:
# print("Evaluate only")
# distmat = test(model, queryloader, galleryloader, train_query_loader, train_gallery_loader,
# use_gpu, return_distmat=True)
# if args.vis_ranked_res:
# visualize_ranked_results(
# distmat, dataset,
# save_dir=osp.join(args.save_dir, 'ranked_results'),
# topk=20,
# )
# return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
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,
optimizer_tmp,
trainloader,
use_gpu,
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, optimizer, trainloader, use_gpu)
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, testloader, queryloader, galleryloader,
train_query_loader, train_gallery_loader, 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,
use_gpu_suo=True,
fpath=osp.join(
args.save_dir, 'checkpoint_ep' + str(epoch + 1) +
checkpoint_suffix + '.pth.tar'))
print("==> Best Rank-1 {:.2%}, 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 main():
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
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))
dataset = mydataset.Market1501(root=args.root, split_id=0)
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
queryloader = DataLoader(
ImageDatasettest(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(
ImageDatasettest(dataset.gallery, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
cri = nn.MSELoss().cuda()
criterion = nn.CrossEntropyLoss()
model = torchreid.resnet_person.net2(num_classes=dataset.num_train_pids)
if args.evaluate:
print("Evaluate only")
checkpoint = torch.load(args.testmodel)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("rank1: {}".format(rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
distmat = test(model,
queryloader,
galleryloader,
use_gpu,
return_distmat=True)
return
trainloader = DataLoader(
ImageDatasettrain(dataset.train, args.height, args.width),
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
if use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = init_optim(args.optim, model.parameters(), args.lr, 5e-04)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=0.1)
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion, cri, optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
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 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
logger_info = LoggerInfo()
sys.stdout = Logger(logger_info)
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("\nInitializing dataset {}".format(args.dataset_plt))
dataset_plt = data_manager.init_imgreid_dataset(root=args.root,
name=args.dataset_plt)
print("\nInitializing dataset {}".format(args.dataset_vecl))
dataset_vecl = data_manager.init_imgreid_dataset(root=args.root,
name=args.dataset_vecl)
transform_test_plt = T.Compose([
T.Resize((args.height_plt, args.width_plt)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# transform_flip_test_plt = T.Compose([
# T.Resize((args.height_plt, args.width_plt)),
# functional.hflip,
# T.ToTensor(),
# T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# ])
transform_test_vecl = T.Compose([
T.Resize((args.height_vecl, args.width_vecl)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# transform_flip_test_vecl = T.Compose([
# T.Resize((args.height_vecl, args.width_vecl)),
# functional.hflip,
# 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
queryloader_plt = DataLoader(
ImageDatasetV2(dataset_plt.query, transform=transform_test_plt),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
# queryloader_flip_plt = DataLoader(
# ImageDatasetV2(dataset_plt.query, transform=transform_flip_test_plt),
# batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
# pin_memory=pin_memory, drop_last=False,
# )
# queryloader_plt = [queryloader_plt, queryloader_flip_plt]
queryloader_plt = [queryloader_plt]
galleryloader_plt = DataLoader(
ImageDatasetV2(dataset_plt.gallery, transform=transform_test_plt),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
# galleryloader_flip_plt = DataLoader(
# ImageDatasetV2(dataset_plt.gallery, transform=transform_flip_test_plt),
# batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
# pin_memory=pin_memory, drop_last=False,
# )
# galleryloader_plt = [galleryloader_plt, galleryloader_flip_plt]
galleryloader_plt = [galleryloader_plt]
queryloader_vecl = DataLoader(
ImageDatasetWGL(dataset_vecl.query,
transform=transform_test_vecl,
with_image_name=True),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
# queryloader_flip_vecl = DataLoader(
# ImageDatasetV2(dataset_vecl.query, transform=transform_flip_test_vecl),
# batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
# pin_memory=pin_memory, drop_last=False,
# )
# queryloader_vecl = [queryloader_vecl, queryloader_flip_vecl]
queryloader_vecl = [queryloader_vecl]
galleryloader_vecl = DataLoader(
ImageDatasetWGL(dataset_vecl.gallery,
transform=transform_test_vecl,
with_image_name=True),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
# galleryloader_flip_vecl = DataLoader(
# ImageDatasetV2(dataset_vecl.gallery, transform=transform_flip_test_vecl),
# batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
# pin_memory=pin_memory, drop_last=False,
# )
# galleryloader_vecl = [galleryloader_vecl, galleryloader_flip_vecl]
galleryloader_vecl = [galleryloader_vecl]
print("\nInitializing model: {}".format(args.arch))
model_plt = models.init_model(name=args.arch_plt,
num_classes=dataset_plt.num_train_pids,
loss_type=args.loss_type)
model_vecl = models.init_model(name=args.arch_vecl,
num_classes=dataset_vecl.num_train_pids,
loss_type=args.loss_type)
print("Plate model size: {:.3f} M".format(count_num_param(model_plt)))
print("Vehicle model size: {:.3f} M".format(count_num_param(model_vecl)))
if args.loss_type == 'xent':
criterion = nn.CrossEntropyLoss()
else:
raise KeyError("Unsupported loss: {}".format(args.loss_type))
if args.resm_plt and args.resm_vecl:
if check_isfile(args.resm_plt) and check_isfile(args.resm_vecl):
ckpt_plt = torch.load(args.resm_plt)
pre_dic_plt = ckpt_plt['state_dict']
model_dic_plt = model_plt.state_dict()
pre_dic_plt = {
k: v
for k, v in pre_dic_plt.items()
if k in model_dic_plt and model_dic_plt[k].size() == v.size()
}
model_dic_plt.update(pre_dic_plt)
model_plt.load_state_dict(model_dic_plt)
args.start_epoch_plt = ckpt_plt['epoch']
rank1_plt = ckpt_plt['rank1']
ckpt_vecl = torch.load(args.resm_vecl)
pre_dic_vecl = ckpt_vecl['state_dict']
model_dic_vecl = model_vecl.state_dict()
pre_dic_vecl = {
k: v
for k, v in pre_dic_vecl.items() if k in model_dic_vecl
and model_dic_vecl[k].size() == v.size()
}
model_dic_vecl.update(pre_dic_vecl)
model_vecl.load_state_dict(model_dic_vecl)
args.start_epoch_vecl = ckpt_vecl['epoch']
rank1_vecl = ckpt_vecl['rank1']
print("\nLoaded checkpoint from '{}' \nand '{}".format(
args.resm_plt, args.resm_vecl))
print("Plate model: start_epoch: {}, rank1: {}".format(
args.start_epoch_plt, rank1_plt))
print("Vehicle model: start_epoch: {}, rank1: {}".format(
args.start_epoch_vecl, rank1_vecl))
if use_gpu:
model_plt = nn.DataParallel(model_plt).cuda()
model_vecl = nn.DataParallel(model_vecl).cuda()
if args.evaluate:
print("\nEvaluate only")
test(model_plt, model_vecl, queryloader_plt, queryloader_vecl,
galleryloader_plt, galleryloader_vecl, use_gpu)
return
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_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,
) #cuhk03_labeled: detected,labeled
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
#pdb.set_trace()
trainloader = DataLoader(
ImageDataset(dataset.train, transform=transform_train),
sampler=RandomIdentitySampler(dataset.train, args.train_batch,
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,
batchsize=args.test_batch,
loss={'xent', 'wcont', 'htri'})
print("Model size: {:.3f} M".format(count_num_param(model)))
criterion_xent = nn.CrossEntropyLoss()
criterion_htri = TripletLoss(margin=args.margin)
criterion_KA = KALoss(margin=args.margin,
same_margin=args.same_margin,
use_auto_samemargin=args.use_auto_samemargin)
cirterion_lifted = LiftedLoss(margin=args.margin)
cirterion_batri = BA_TripletLoss(margin=args.margin)
if args.use_auto_samemargin == True:
G_params = [{
'params': model.parameters(),
'lr': args.lr
}, {
'params': criterion_KA.auto_samemargin,
'lr': args.lr
}]
else:
G_params = [para for _, para in model.named_parameters()]
optimizer = init_optim(args.optim, G_params, args.lr, args.weight_decay)
if args.load_weights:
# load pretrained weights but ignore layers that don't match in size
if check_isfile(args.load_weights):
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:
if check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(
args.start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
distmat = test(model,
queryloader,
galleryloader,
use_gpu,
return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
adjust_learning_rate(optimizer, epoch)
train(epoch, model, cirterion_batri, cirterion_lifted, criterion_xent,
criterion_htri, criterion_KA, optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
if (epoch + 1) > args.start_eval and args.eval_step > 0 and (
epoch + 1) % args.eval_step == 0 or (epoch +
1) == args.max_epoch:
rank1 = 0
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
print("==> Test")
sys.stdout.flush()
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("model saved")
print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(
best_rank1, best_epoch))
sys.stdout.flush()
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))
sys.stdout.flush()
def main(args):
args = parser.parse_args(args)
#global best_rank1
best_rank1 = -np.inf
torch.manual_seed(args.seed)
# np.random.seed(args.seed)
# 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
if not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
else:
test_dir = args.save_dir
if args.save_dir == 'log':
if args.resume:
test_dir = os.path.dirname(args.resume)
else:
test_dir = os.path.dirname(args.load_weights)
sys.stdout = Logger(osp.join(test_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)
# print("Currently using GPU {}".format(args.gpu_devices))
# #cudnn.benchmark = False
# cudnn.deterministic = True
# torch.cuda.manual_seed_all(args.seed)
# torch.set_default_tensor_type('torch.DoubleTensor')
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(),
#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
if 'stanford' in args.dataset:
datasetLoader = ImageDataset_stanford
else:
datasetLoader = ImageDataset
if args.crop_img:
print("Using Cropped Images")
else:
print("NOT using cropped Images")
trainloader = DataLoader(
datasetLoader(dataset.train,
-1,
crop=args.crop_img,
transform=transform_train),
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
testloader = DataLoader(
datasetLoader(dataset.test,
-1,
crop=args.crop_img,
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', 'angular'} if args.use_angular else {'xent'},
use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
if not (args.use_angular):
if args.label_smooth:
print("Using Label Smoothing")
criterion = CrossEntropyLabelSmooth(
num_classes=dataset.num_train_pids, use_gpu=use_gpu)
else:
criterion = nn.CrossEntropyLoss()
else:
if args.label_smooth:
print("Using Label Smoothing")
criterion = AngularLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
else:
criterion = AngleLoss()
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
if args.scheduler != 0:
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,
list(model.classifier.parameters()) +
list(model.encoder.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")
test_dir = args.save_dir
if args.save_dir == 'log':
if args.resume:
test_dir = os.path.dirname(args.resume)
else:
test_dir = os.path.dirname(args.load_weights)
distmat = test(model,
testloader,
use_gpu,
args,
writer=None,
epoch=-1,
return_distmat=True,
draw_tsne=args.draw_tsne,
tsne_clusters=args.tsne_labels,
use_cosine=args.plot_deltaTheta)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(test_dir, 'ranked_results'),
topk=10,
)
if args.plot_deltaTheta:
plot_deltaTheta(distmat,
dataset,
save_dir=osp.join(test_dir, 'deltaTheta_results'),
min_rank=1)
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.test_rot:
print("Training only classifier for rotation")
model = models.init_model(name='rot_tester',
base_model=model,
inplanes=2048,
num_rot_classes=8)
criterion_rot = nn.CrossEntropyLoss()
optimizer_rot = init_optim(args.optim, model.fc_rot.parameters(),
args.fixbase_lr, args.weight_decay)
if use_gpu:
model = nn.DataParallel(model).cuda()
try:
best_epoch = 0
for epoch in range(0, args.max_epoch):
start_train_time = time.time()
train_rotTester(epoch, model, criterion_rot, optimizer_rot,
trainloader, use_gpu, writer, args)
train_time += round(time.time() - start_train_time)
if args.scheduler != 0:
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:
if (epoch + 1) == args.max_epoch:
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': -1,
'epoch': epoch,
}, False,
osp.join(
args.save_dir, 'beforeTesting_checkpoint_ep' +
str(epoch + 1) + '.pth.tar'))
print("==> Test")
rank1 = test_rotTester(model,
criterion_rot,
queryloader,
galleryloader,
trainloader,
use_gpu,
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 Cccuracy {:.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
except KeyboardInterrupt:
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': -1,
'epoch': epoch,
}, False,
osp.join(
args.save_dir, 'keyboardInterrupt_checkpoint_ep' +
str(epoch + 1) + '.pth.tar'))
return None, None
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,
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")
best_epoch = 0
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion, optimizer, trainloader, use_gpu, writer,
args)
train_time += round(time.time() - start_train_time)
if args.scheduler != 0:
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:
if (epoch + 1) == args.max_epoch:
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': -1,
'epoch': epoch,
}, False,
osp.join(
args.save_dir, 'beforeTesting_checkpoint_ep' +
str(epoch + 1) + '.pth.tar'))
print("==> Test")
rank1 = test(model,
testloader,
use_gpu,
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 main(args):
args = parser.parse_args(args)
#global best_rank1
best_rank1 = -np.inf
torch.manual_seed(args.seed)
# np.random.seed(args.seed)
# 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
if not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
else:
test_dir = args.save_dir
if args.save_dir == 'log':
if args.resume:
test_dir = os.path.dirname(args.resume)
else:
test_dir = os.path.dirname(args.load_weights)
sys.stdout = Logger(osp.join(test_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,
split_wild=args.split_wild)
transform_train = T.Compose([
T.Random2DTranslation(args.height, args.width),
#T.Resize((args.height, args.width)),
T.RandomSizedEarser(),
T.RandomHorizontalFlip_custom(),
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,
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', 'angular'} if args.use_angular else {'xent'},
use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
use_autoTune = False
if not (args.use_angular):
if args.label_smooth:
print("Using Label Smoothing with epsilon", args.label_epsilon)
criterion = CrossEntropyLabelSmooth(
num_classes=dataset.num_train_pids,
epsilon=args.label_epsilon,
use_gpu=use_gpu)
elif args.focal_loss:
print("Using Focal Loss with gamma=", args.focal_gamma)
criterion = FocalLoss(gamma=args.focal_gamma)
else:
print("Using Normal Cross-Entropy")
criterion = nn.CrossEntropyLoss()
if args.jsd:
print("Using JSD regularizer")
criterion = (criterion, JSD_loss(dataset.num_train_pids))
if args.auto_tune_mtl:
print("Using AutoTune")
use_autoTune = True
criterion = MultiHeadLossAutoTune(
list(criterion),
[args.lambda_xent, args.confidence_beta]).cuda()
else:
if args.confidence_penalty:
print("Using Confidence Penalty", args.confidence_beta)
criterion = (criterion, ConfidencePenalty())
if args.auto_tune_mtl and args.confidence_penalty:
print("Using AutoTune")
use_autoTune = True
criterion = MultiHeadLossAutoTune(
list(criterion),
[args.lambda_xent, -args.confidence_beta]).cuda()
else:
if args.label_smooth:
print("Using Angular Label Smoothing")
criterion = AngularLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
else:
print("Using Angular Loss")
criterion = AngleLoss()
if use_autoTune:
optimizer = init_optim(
args.optim,
list(model.parameters()) + list(criterion.parameters()), args.lr,
args.weight_decay)
else:
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
if args.scheduler:
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):
if use_autoTune:
optimizer_tmp = init_optim(
args.optim,
list(model.classifier.parameters()) +
list(criterion.parameters()), args.fixbase_lr,
args.weight_decay)
else:
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.single_folder != '':
extract_features(model,
use_gpu,
args,
transform_test,
return_distmat=False)
return
if args.evaluate:
print("Evaluate only")
test_dir = args.save_dir
if args.save_dir == 'log':
if args.resume:
test_dir = os.path.dirname(args.resume)
else:
test_dir = os.path.dirname(args.load_weights)
distmat = test(model,
queryloader,
galleryloader,
use_gpu,
args,
writer=None,
epoch=-1,
return_distmat=True,
tsne_clusters=args.tsne_labels)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(test_dir, 'ranked_results'),
topk=10,
)
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,
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")
best_epoch = 0
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion, optimizer, trainloader, use_gpu, writer,
args)
train_time += round(time.time() - start_train_time)
if args.scheduler:
scheduler.step()
if (epoch + 1) > args.start_eval and (
(args.save_epoch > 0 and (epoch + 1) % args.save_epoch == 0) or
(args.eval_step > 0 and (epoch + 1) % args.eval_step == 0) or
(epoch + 1) == args.max_epoch):
if (epoch + 1) == args.max_epoch:
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': -1,
'epoch': epoch,
}, False,
osp.join(
args.save_dir, 'beforeTesting_checkpoint_ep' +
str(epoch + 1) + '.pth.tar'))
is_best = False
rank1 = -1
if args.eval_step > 0:
print("==> Test")
rank1 = test(model,
queryloader,
galleryloader,
use_gpu,
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 main():
global args, best_rank1
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_vidreid_dataset(root=args.root,
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
# decompose tracklets into images for image-based training
new_train = []
for img_paths, pid, camid in dataset.train:
for img_path in img_paths:
new_train.append((img_path, pid, camid))
trainloader = DataLoader(
ImageDataset(new_train, transform=transform_train),
sampler=RandomIdentitySampler(new_train, args.train_batch,
args.num_instances),
batch_size=args.train_batch,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
VideoDataset(dataset.query,
seq_len=args.seq_len,
sample='evenly',
transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
VideoDataset(dataset.gallery,
seq_len=args.seq_len,
sample='evenly',
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: {:.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()
criterion_htri = TripletLoss(margin=args.margin)
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.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,
args.pool,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
train_time = 0
best_epoch = args.start_epoch
print("==> Start 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)
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, args.pool, 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 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_imgreid_dataset(root=args.root,
name=args.dataset,
split_id=args.split_id)
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,
)
testloader = DataLoader(
ImageDataset(dataset.test, 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, loss={'xent'}, use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
gender_criterion_xent = nn.CrossEntropyLoss()
staff_criterion_xent = nn.CrossEntropyLoss()
customer_criterion_xent = nn.CrossEntropyLoss()
stand_criterion_xent = nn.CrossEntropyLoss()
sit_criterion_xent = nn.CrossEntropyLoss()
phone_criterion_xent = nn.CrossEntropyLoss()
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if use_gpu:
model = nn.DataParallel(model).cuda()
start_time = time.time()
train_time = 0
best_score = 0
best_epoch = args.start_epoch
print("==> Start training")
################################### 修改到这里，把train 和 test改一下就好
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, gender_criterion_xent, staff_criterion_xent, customer_criterion_xent, \
stand_criterion_xent, sit_criterion_xent, phone_criterion_xent, optimizer, trainloader, use_gpu)
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")
gender_accurary, staff_accurary, customer_accurary, stand_accurary, sit_accurary, phone_accurary = test(
model, testloader, use_gpu)
Score = (gender_accurary + staff_accurary + customer_accurary +
stand_accurary + sit_accurary + phone_accurary) * 100
is_best = Score > best_score
if is_best:
best_score = Score
best_gender_acc = gender_accurary
best_staff_acc = staff_accurary
best_customer_acc = customer_accurary
best_stand_acc = stand_accurary
best_sit_acc = sit_accurary
best_phone_acc = phone_accurary
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': Score,
'epoch': epoch,
}, is_best,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
print(
"==> Best best_score {} |Gender_acc {}\t Staff_acc {}\t Customer_acc {}\t Stand_acc {}\t Sit_acc {}\t Phone_acc {}|achieved at epoch {}"
.format(best_score, best_gender_acc, best_staff_acc, best_customer_acc,
best_stand_acc, best_sit_acc, best_phone_acc, 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 main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--snap_shot',
type=str,
default='saved-models/densenet121_xent_market1501.pth.tar')
parser.add_argument('--arch', type=str, default='densenet121')
parser.add_argument('--dataset-path',
type=str,
default='data/valset/valSet')
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('--test-batch',
default=100,
type=int,
help="test batch size")
parser.add_argument('-j',
'--workers',
default=4,
type=int,
help="number of data loading workers (default: 4)")
parser.add_argument('--log-dir', type=str, default='log/eval_625')
parser.add_argument('--gpu', type=int, default=1)
args = parser.parse_args()
pin_memory = True if args.gpu else False
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch,
num_classes=751,
loss={'xent'},
use_gpu=args.gpu).cuda()
print("Model size: {:.3f} M".format(count_num_param(model)))
checkpoint = torch.load(args.snap_shot)
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.snap_shot))
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]),
])
queryloader = DataLoader(
evalDataset(os.path.join(args.dataset_path, 'query'),
transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
evalDataset(os.path.join(args.dataset_path, 'gallery'),
transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
dataloaders = {'query': queryloader, 'gallery': galleryloader}
for dataset in ['val']:
for subset in ['query', 'gallery']:
test_names, test_features = extractor(model, dataloaders[subset])
results = {'names': test_names, 'features': test_features.numpy()}
scipy.io.savemat(
os.path.join(args.log_dir,
'feature_%s_%s.mat' % (dataset, subset)), results)
def main():
global best_rank1, best_mAP
random.seed(args.seed)
np.random.seed(args.seed)
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'.format(time.strftime('-%Y-%m-%d-%H-%M-%S'))))
else:
sys.stdout = Logger(
osp.join(
args.save_dir,
'log_test{}.txt'.format(time.strftime('-%Y-%m-%d-%H-%M-%S'))))
writer = SummaryWriter(log_dir=args.save_dir, comment=args.arch)
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = False if 'resnet3dt' in args.arch else 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_vidreid_dataset(root=args.root,
name=args.dataset,
split_id=args.split_id,
use_pose=args.use_pose)
transform_train = list()
print('Transform:')
if args.misalign_aug:
print('+ Misalign Augmentation')
transform_train.append(T.GroupMisAlignAugment())
if args.rand_crop:
print('+ Random Crop')
transform_train.append(T.GroupRandomCrop(size=(240, 120)))
print('+ Resize to ({} x {})'.format(args.height, args.width))
transform_train.append(T.GroupResize((args.height, args.width)))
if args.flip_aug:
print('+ Random HorizontalFlip')
transform_train.append(T.GroupRandomHorizontalFlip())
print('+ ToTensor')
transform_train.append(T.GroupToTensor())
print(
'+ Normalize with mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]'
)
transform_train.append(
T.GroupNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]))
if args.rand_erase:
print('+ Random Erasing')
transform_train.append(T.GroupRandomErasing())
transform_train = T.Compose(transform_train)
transform_test = T.Compose([
T.GroupResize((args.height, args.width)),
T.GroupToTensor(),
T.GroupNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]),
])
pin_memory = True if use_gpu else False
trainloader = DataLoader(
VideoDataset(dataset.train,
seq_len=args.seq_len,
sample=args.train_sample,
transform=transform_train,
training=True,
pose_info=dataset.process_poses,
num_split=args.num_split,
num_parts=args.num_parts,
num_scale=args.num_scale,
pyramid_part=args.pyramid_part,
enable_pose=args.use_pose),
sampler=eval(args.train_sampler)(dataset.train,
batch_size=args.train_batch,
num_instances=args.num_instances),
batch_size=args.train_batch,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
VideoDataset(dataset.query,
seq_len=args.seq_len,
sample=args.test_sample,
transform=transform_test,
pose_info=dataset.process_poses,
num_split=args.num_split,
num_parts=args.num_parts,
num_scale=args.num_scale,
pyramid_part=args.pyramid_part,
enable_pose=args.use_pose),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
VideoDataset(dataset.gallery,
seq_len=args.seq_len,
sample=args.test_sample,
transform=transform_test,
pose_info=dataset.process_poses,
num_split=args.num_split,
num_parts=args.num_parts,
num_scale=args.num_scale,
pyramid_part=args.pyramid_part,
enable_pose=args.use_pose),
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'},
last_stride=args.last_stride,
num_parts=args.num_parts,
num_scale=args.num_scale,
num_split=args.num_split,
pyramid_part=args.pyramid_part,
num_gb=args.num_gb,
use_pose=args.use_pose,
learn_graph=args.learn_graph,
consistent_loss=args.consistent_loss,
bnneck=args.bnneck,
save_dir=args.save_dir)
input_size = sum(calc_splits(
args.num_split)) if args.pyramid_part else args.num_split
input_size *= args.num_scale * args.seq_len
num_params, flops = compute_model_complexity(
model,
input=[
torch.randn(1, args.seq_len, 3, args.height, args.width),
torch.ones(1, input_size, input_size)
],
verbose=True,
only_conv_linear=False)
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.label_smooth:
criterion_xent = CrossEntropyLabelSmooth(
num_classes=dataset.num_train_pids, use_gpu=use_gpu)
else:
criterion_xent = nn.CrossEntropyLoss()
criterion_htri = TripletLoss(margin=args.margin, soft=args.soft_margin)
param_groups = model.parameters()
optimizer = init_optim(args.optim, param_groups, args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if args.warmup:
scheduler = lr_scheduler.WarmupMultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma,
warmup_iters=10,
warmup_factor=0.01)
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):
print("Loaded checkpoint from '{}'".format(args.resume))
from functools import partial
import pickle
pickle.load = partial(pickle.load, encoding="latin1")
pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
checkpoint = torch.load(args.resume,
map_location=lambda storage, loc: storage,
pickle_module=pickle)
print('Loaded model weights')
model.load_state_dict(checkpoint['state_dict'])
if optimizer is not None and 'optimizer' in checkpoint:
print('Loaded optimizer')
optimizer.load_state_dict(checkpoint['optimizer'])
if use_gpu:
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
start_epoch = checkpoint['epoch'] + 1
print('- start_epoch: {}'.format(start_epoch))
best_rank1 = checkpoint['rank1']
print("- rank1: {}".format(best_rank1))
if 'mAP' in checkpoint:
best_mAP = checkpoint['mAP']
print("- mAP: {}".format(best_mAP))
else:
start_epoch = 0
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
distmat = test(model,
queryloader,
galleryloader,
args.pool,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
train_time = 0
best_epoch = start_epoch
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,
writer=writer)
train_time += round(time.time() - start_train_time)
if epoch >= args.zero_wd > 0:
set_wd(optimizer, 0)
for group in optimizer.param_groups:
assert group['weight_decay'] == 0, '{} is not zero'.format(
group['weight_decay'])
scheduler.step(epoch)
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, mAP = test(model, queryloader, galleryloader, args.pool,
use_gpu)
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_mAP = mAP
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,
'optimizer': optimizer.state_dict(),
'rank1': rank1,
'mAP': mAP,
'epoch': epoch,
}, False,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
writer.add_scalar(tag='acc/rank1',
scalar_value=rank1,
global_step=epoch + 1)
writer.add_scalar(tag='acc/mAP',
scalar_value=mAP,
global_step=epoch + 1)
print("==> Best Rank-1 {:.2%}, mAP: {:.2%}, achieved at epoch {}".format(
best_rank1, best_mAP, 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))
