def main(args: argparse.Namespace):
logger = CompleteLogger(args.log, args.phase)
print(args)
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
train_transform = utils.get_train_transform(args.height,
args.width,
args.train_resizing,
random_horizontal_flip=True,
random_color_jitter=False,
random_gray_scale=False,
random_erasing=True)
val_transform = utils.get_val_transform(args.height, args.width)
print("train_transform: ", train_transform)
print("val_transform: ", val_transform)
working_dir = osp.dirname(osp.abspath(__file__))
source_root = osp.join(working_dir, args.source_root)
target_root = osp.join(working_dir, args.target_root)
# source dataset
source_dataset = datasets.__dict__[args.source](
root=osp.join(source_root, args.source.lower()))
sampler = RandomMultipleGallerySampler(source_dataset.train,
args.num_instances)
train_source_loader = DataLoader(convert_to_pytorch_dataset(
source_dataset.train,
root=source_dataset.images_dir,
transform=train_transform),
batch_size=args.batch_size,
num_workers=args.workers,
sampler=sampler,
pin_memory=True,
drop_last=True)
train_source_iter = ForeverDataIterator(train_source_loader)
cluster_source_loader = DataLoader(convert_to_pytorch_dataset(
source_dataset.train,
root=source_dataset.images_dir,
transform=val_transform),
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
pin_memory=True)
val_loader = DataLoader(convert_to_pytorch_dataset(
list(set(source_dataset.query) | set(source_dataset.gallery)),
root=source_dataset.images_dir,
transform=val_transform),
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
pin_memory=True)
# target dataset
target_dataset = datasets.__dict__[args.target](
root=osp.join(target_root, args.target.lower()))
cluster_target_loader = DataLoader(convert_to_pytorch_dataset(
target_dataset.train,
root=target_dataset.images_dir,
transform=val_transform),
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
pin_memory=True)
test_loader = DataLoader(convert_to_pytorch_dataset(
list(set(target_dataset.query) | set(target_dataset.gallery)),
root=target_dataset.images_dir,
transform=val_transform),
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
pin_memory=True)
n_s_classes = source_dataset.num_train_pids
args.n_classes = n_s_classes + len(target_dataset.train)
args.n_s_classes = n_s_classes
args.n_t_classes = len(target_dataset.train)
# create model
backbone = models.__dict__[args.arch](pretrained=True)
pool_layer = nn.Identity() if args.no_pool else None
model = ReIdentifier(backbone,
args.n_classes,
finetune=args.finetune,
pool_layer=pool_layer)
features_dim = model.features_dim
idm_bn_names = filter_layers(args.stage)
convert_dsbn_idm(model, idm_bn_names, idm=False)
model = model.to(device)
model = DataParallel(model)
# resume from the best checkpoint
if args.phase != 'train':
checkpoint = torch.load(logger.get_checkpoint_path('best'),
map_location='cpu')
utils.copy_state_dict(model, checkpoint['model'])
# analysis the model
if args.phase == 'analysis':
# plot t-SNE
utils.visualize_tsne(source_loader=val_loader,
target_loader=test_loader,
model=model,
filename=osp.join(logger.visualize_directory,
'analysis', 'TSNE.pdf'),
device=device)
# visualize ranked results
visualize_ranked_results(test_loader,
model,
target_dataset.query,
target_dataset.gallery,
device,
visualize_dir=logger.visualize_directory,
width=args.width,
height=args.height,
rerank=args.rerank)
return
if args.phase == 'test':
print("Test on target domain:")
validate(test_loader,
model,
target_dataset.query,
target_dataset.gallery,
device,
cmc_flag=True,
rerank=args.rerank)
return
# create XBM
dataset_size = len(source_dataset.train) + len(target_dataset.train)
memory_size = int(args.ratio * dataset_size)
xbm = XBM(memory_size, features_dim)
# initialize source-domain class centroids
source_feature_dict = extract_reid_feature(cluster_source_loader,
model,
device,
normalize=True)
source_features_per_id = {}
for f, pid, _ in source_dataset.train:
if pid not in source_features_per_id:
source_features_per_id[pid] = []
source_features_per_id[pid].append(source_feature_dict[f].unsqueeze(0))
source_centers = [
torch.cat(source_features_per_id[pid], 0).mean(0)
for pid in sorted(source_features_per_id.keys())
]
source_centers = torch.stack(source_centers, 0)
source_centers = F.normalize(source_centers, dim=1)
model.module.head.weight.data[0:n_s_classes].copy_(
source_centers.to(device))
# save memory
del source_centers, cluster_source_loader, source_features_per_id
# define optimizer and lr scheduler
optimizer = Adam(model.module.get_parameters(base_lr=args.lr,
rate=args.rate),
args.lr,
weight_decay=args.weight_decay)
lr_scheduler = StepLR(optimizer, step_size=args.step_size, gamma=0.1)
if args.resume:
checkpoint = torch.load(args.resume, map_location='cpu')
utils.copy_state_dict(model, checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
# start training
best_test_mAP = 0.
for epoch in range(args.start_epoch, args.epochs):
# run clustering algorithm and generate pseudo labels
train_target_iter = run_dbscan(cluster_target_loader, model,
target_dataset, train_transform, args)
# train for one epoch
print(lr_scheduler.get_lr())
train(train_source_iter, train_target_iter, model, optimizer, xbm,
epoch, args)
if (epoch + 1) % args.eval_step == 0 or (epoch == args.epochs - 1):
# remember best mAP and save checkpoint
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch
}, logger.get_checkpoint_path(epoch))
print("Test on target domain...")
_, test_mAP = validate(test_loader,
model,
target_dataset.query,
target_dataset.gallery,
device,
cmc_flag=True,
rerank=args.rerank)
if test_mAP > best_test_mAP:
shutil.copy(logger.get_checkpoint_path(epoch),
logger.get_checkpoint_path('best'))
best_test_mAP = max(test_mAP, best_test_mAP)
# update lr
lr_scheduler.step()
print("best mAP on target = {}".format(best_test_mAP))
logger.close()
def load_pretrain_params(self):
success_layer = copy_state_dict(self.state_dict(),
torch.load(config.hrnet_pretrain),
prefix='',
fix_loaded=True)
from utils import masks_to_segmentation, overaly_segmentation, load_segmentation_definition, copy_state_dict
# Command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", default='./out/weights.pt', type=str)
parser.add_argument("--data_directory", default='./data/original/', type=str)
parser.add_argument("--result_directory", default='./result/', type=str)
args = parser.parse_args()
if not os.path.isdir(args.result_directory):
os.mkdir(args.result_directory)
model = createDeepLabv3(outputchannels=12, pretrain=True)
# model = torch.nn.DataParallel(model)
train_model = torch.load(args.model_path)
trained_state_dict = copy_state_dict(train_model.state_dict())
model.load_state_dict(trained_state_dict)
model.eval()
model.cuda()
definitions = load_segmentation_definition(
'data/json/annotation_definitions.json')
s0 = time.time()
with torch.no_grad():
for step, path in enumerate(glob.glob(args.data_directory + '*.png')):
im = cv2.imread(path)
im = cv2.resize(im, (480, 320), cv2.INTER_NEAREST)
input_tensor = torch.from_numpy(im)
input_tensor = input_tensor.type(torch.FloatTensor) / 255
input_tensor = input_tensor.cuda()
input_tensor = input_tensor.permute(2, 0, 1)
def main(args: argparse.Namespace):
logger = CompleteLogger(args.log, args.phase)
print(args)
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
train_transform = utils.get_train_transform(args.height,
args.width,
args.train_resizing,
random_horizontal_flip=True,
random_color_jitter=False,
random_gray_scale=False,
random_erasing=True)
val_transform = utils.get_val_transform(args.height, args.width)
print("train_transform: ", train_transform)
print("val_transform: ", val_transform)
working_dir = osp.dirname(osp.abspath(__file__))
source_root = osp.join(working_dir, args.source_root)
target_root = osp.join(working_dir, args.target_root)
# source dataset
source_dataset = datasets.__dict__[args.source](
root=osp.join(source_root, args.source.lower()))
val_loader = DataLoader(convert_to_pytorch_dataset(
list(set(source_dataset.query) | set(source_dataset.gallery)),
root=source_dataset.images_dir,
transform=val_transform),
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
pin_memory=True)
# target dataset
target_dataset = datasets.__dict__[args.target](
root=osp.join(target_root, args.target.lower()))
cluster_loader = DataLoader(convert_to_pytorch_dataset(
target_dataset.train,
root=target_dataset.images_dir,
transform=val_transform),
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
pin_memory=True)
test_loader = DataLoader(convert_to_pytorch_dataset(
list(set(target_dataset.query) | set(target_dataset.gallery)),
root=target_dataset.images_dir,
transform=val_transform),
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
pin_memory=True)
# create model
num_classes = args.num_clusters
backbone = utils.get_model(args.arch)
pool_layer = nn.Identity() if args.no_pool else None
model = ReIdentifier(backbone,
num_classes,
finetune=args.finetune,
pool_layer=pool_layer).to(device)
model = DataParallel(model)
# load pretrained weights
pretrained_model = torch.load(args.pretrained_model_path)
utils.copy_state_dict(model, pretrained_model)
# resume from the best checkpoint
if args.phase != 'train':
checkpoint = torch.load(logger.get_checkpoint_path('best'),
map_location='cpu')
utils.copy_state_dict(model, checkpoint['model'])
# analysis the model
if args.phase == 'analysis':
# plot t-SNE
utils.visualize_tsne(source_loader=val_loader,
target_loader=test_loader,
model=model,
filename=osp.join(logger.visualize_directory,
'analysis', 'TSNE.pdf'),
device=device)
# visualize ranked results
visualize_ranked_results(test_loader,
model,
target_dataset.query,
target_dataset.gallery,
device,
visualize_dir=logger.visualize_directory,
width=args.width,
height=args.height,
rerank=args.rerank)
return
if args.phase == 'test':
print("Test on Source domain:")
validate(val_loader,
model,
source_dataset.query,
source_dataset.gallery,
device,
cmc_flag=True,
rerank=args.rerank)
print("Test on target domain:")
validate(test_loader,
model,
target_dataset.query,
target_dataset.gallery,
device,
cmc_flag=True,
rerank=args.rerank)
return
# define loss function
criterion_ce = CrossEntropyLossWithLabelSmooth(num_classes).to(device)
criterion_triplet = SoftTripletLoss(margin=args.margin).to(device)
# optionally resume from a checkpoint
if args.resume:
checkpoint = torch.load(args.resume, map_location='cpu')
utils.copy_state_dict(model, checkpoint['model'])
args.start_epoch = checkpoint['epoch'] + 1
# start training
best_test_mAP = 0.
for epoch in range(args.start_epoch, args.epochs):
# run clustering algorithm and generate pseudo labels
if args.clustering_algorithm == 'kmeans':
train_target_iter = run_kmeans(cluster_loader, model,
target_dataset, train_transform,
args)
elif args.clustering_algorithm == 'dbscan':
train_target_iter, num_classes = run_dbscan(
cluster_loader, model, target_dataset, train_transform, args)
# define cross entropy loss with current number of classes
criterion_ce = CrossEntropyLossWithLabelSmooth(num_classes).to(device)
# define optimizer
optimizer = Adam(model.module.get_parameters(base_lr=args.lr,
rate=args.rate),
args.lr,
weight_decay=args.weight_decay)
# train for one epoch
train(train_target_iter, model, optimizer, criterion_ce,
criterion_triplet, epoch, args)
if (epoch + 1) % args.eval_step == 0 or (epoch == args.epochs - 1):
# remember best mAP and save checkpoint
torch.save({
'model': model.state_dict(),
'epoch': epoch
}, logger.get_checkpoint_path(epoch))
print("Test on target domain...")
_, test_mAP = validate(test_loader,
model,
target_dataset.query,
target_dataset.gallery,
device,
cmc_flag=True,
rerank=args.rerank)
if test_mAP > best_test_mAP:
shutil.copy(logger.get_checkpoint_path(epoch),
logger.get_checkpoint_path('best'))
best_test_mAP = max(test_mAP, best_test_mAP)
print("best mAP on target = {}".format(best_test_mAP))
logger.close()
alphas = ign_alphas(net, args)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume is not None:
checkpoint_path = args.resume
if not os.path.isfile(checkpoint_path):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
args.start_epochs = checkpoint['epoch'] + 1
if torch.cuda.device_count() > 1:
copy_state_dict(net, checkpoint, parallel=True)
else:
copy_state_dict(net, checkpoint)
# copy_optimizer_state_dict(optimizer, checkpoint['optimizer'])
optimizer.load_state_dict(checkpoint['optimizer'])
# copy_optimizer_state_dict(alphas.optimizer, checkpoint['alpha_optimizer'])
alphas.optimizer.load_state_dict(checkpoint['alpha_optimizer'])
# params = net.parameters()
# for param in params:
# # print(param)
# print(param.shape)
if args.scheduler == 'step':
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[75, 125, 175], gamma=0.1)
elif args.scheduler == 'cos':