def load_network(net_path, **kwargs):
"""Load network for tracking.
args:
net_path - Path to network. If it is not an absolute path, it is relative to the network_path in the local.py.
See ltr.admin.loading.load_network for further details.
**kwargs - Additional key-word arguments that are sent to ltr.admin.loading.load_network.
"""
kwargs['backbone_pretrained'] = False
if os.path.isabs(net_path):
path_full = net_path
net, _ = ltr_loading.load_network(path_full, **kwargs)
elif isinstance(env_settings().network_path, (list, tuple)):
net = None
for p in env_settings().network_path:
path_full = os.path.join(p, net_path)
try:
net, _ = ltr_loading.load_network(path_full, **kwargs)
break
except Exception as e:
# print(e)
pass
assert net is not None, 'Failed to load network'
else:
path_full = os.path.join(env_settings().network_path, net_path)
net, _ = ltr_loading.load_network(path_full, **kwargs)
return net
def load_network(net_path):
if os.path.isabs(net_path):
path_full = net_path
net, _ = ltr_loading.load_network(path_full, backbone_pretrained=False)
elif isinstance(env_settings().network_path, (list, tuple)):
net = None
for p in env_settings().network_path:
path_full = os.path.join(p, net_path)
try:
net, _ = ltr_loading.load_network(path_full, backbone_pretrained=False)
break
except:
pass
assert net is not None, 'Failed to load network'
else:
path_full = os.path.join(env_settings().network_path, net_path)
net, _ = ltr_loading.load_network(path_full, backbone_pretrained=False)
return net
def load_network(self):
self.net, _ = ltr_loading.load_network(self.net_path, **self.net_kwargs)
if self.use_gpu:
self.cuda()
self.eval()
def run(settings):
settings.move_data_to_gpu = False
settings.description = ''
settings.batch_size = 10
settings.test_sequence_length = 50
settings.num_workers = 8
settings.print_interval = 1
settings.normalize_mean = [0.485, 0.456, 0.406]
settings.normalize_std = [0.229, 0.224, 0.225]
settings.search_area_factor = 5.0
settings.output_sigma_factor = 1 / 4
settings.target_filter_sz = 4
settings.feature_sz = 18
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_param = {
'train_mode': 'uniform',
'train_factor': 3.0,
'train_limit_motion': False,
'test_mode': 'uniform',
'test_factor': 4.5,
'test_limit_motion': True
}
settings.scale_jitter_param = {'train_factor': 0.25, 'test_factor': 0.3}
settings.hinge_threshold = 0.05
settings.print_stats = [
'Loss/total', 'Loss/raw/test_clf', 'Loss/raw/test_clf_acc',
'Loss/raw/dimp_clf_acc', 'Loss/raw/is_target',
'Loss/raw/is_target_after_prop', 'Loss/raw/test_seq_acc',
'Loss/raw/dimp_seq_acc'
]
lasot_train = Lasot(settings.env.lasot_dir, split='train')
got10k_train = Got10k(settings.env.got10k_dir, split='vottrain')
trackingnet_train = TrackingNet(settings.env.trackingnet_dir,
set_ids=[0, 1, 2, 3, 4])
# Validation datasets
got10k_val = Got10k(settings.env.got10k_dir, split='votval')
# Data transform
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05))
transform_train = tfm.Transform(
tfm.ToTensorAndJitter(0.2),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
transform_val = tfm.Transform(
tfm.ToTensor(),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
# The tracking pairs processing module
output_sigma = settings.output_sigma_factor / settings.search_area_factor
proposal_params = None
label_params = {
'feature_sz': settings.feature_sz,
'sigma_factor': output_sigma,
'kernel_sz': settings.target_filter_sz,
'end_pad_if_even': True
}
data_processing_train = processing.KYSProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_param=settings.center_jitter_param,
scale_jitter_param=settings.scale_jitter_param,
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_train,
joint_transform=transform_joint,
min_crop_inside_ratio=0.1)
data_processing_val = processing.KYSProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_param=settings.center_jitter_param,
scale_jitter_param=settings.scale_jitter_param,
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_val,
joint_transform=transform_joint,
min_crop_inside_ratio=0.1)
# Train sampler and loader
sequence_sample_info = {
'num_train_frames': 3,
'num_test_frames': settings.test_sequence_length,
'max_train_gap': 30,
'allow_missing_target': True,
'min_fraction_valid_frames': 0.5,
'mode': 'Sequence'
}
dataset_train = sampler.KYSSampler(
[got10k_train, trackingnet_train, lasot_train], [0.3, 0.3, 0.25],
samples_per_epoch=settings.batch_size * 150,
sequence_sample_info=sequence_sample_info,
processing=data_processing_train,
sample_occluded_sequences=True)
loader_train = LTRLoader('train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
shuffle=True,
drop_last=True,
stack_dim=1)
# Validation samplers and loaders
dataset_val = sampler.KYSSampler([got10k_val], [1],
samples_per_epoch=1000,
sequence_sample_info=sequence_sample_info,
processing=data_processing_val,
sample_occluded_sequences=True)
loader_val = LTRLoader('val',
dataset_val,
training=False,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
shuffle=False,
drop_last=True,
epoch_interval=5,
stack_dim=1)
# load base dimp
dimp_weights_path = os.path.join(settings.env.pretrained_networks,
'dimp50.pth')
base_net, _ = network_loading.load_network(checkpoint=dimp_weights_path)
net = kysnet_models.kysnet_res50(optim_iter=3,
cv_kernel_size=3,
cv_max_displacement=9,
cv_stride=1,
init_gauss_sigma=output_sigma *
settings.feature_sz,
train_feature_extractor=False,
train_iounet=False,
detach_length=0,
state_dim=8,
representation_predictor_dims=(16, ),
conf_measure='entropy',
dimp_thresh=0.05)
# Move pre-trained dimp weights
net.backbone_feature_extractor.load_state_dict(
base_net.feature_extractor.state_dict())
net.dimp_classifier.load_state_dict(base_net.classifier.state_dict())
net.bb_regressor.load_state_dict(base_net.bb_regressor.state_dict())
# To be safe
for p in net.backbone_feature_extractor.parameters():
p.requires_grad_(False)
for p in net.dimp_classifier.parameters():
p.requires_grad_(False)
for p in net.bb_regressor.parameters():
p.requires_grad_(False)
objective = {
'test_clf':
ltr_losses.LBHingev2(threshold=settings.hinge_threshold,
return_per_sequence=False),
'dimp_clf':
ltr_losses.LBHingev2(threshold=settings.hinge_threshold,
return_per_sequence=False),
'is_target':
ltr_losses.IsTargetCellLoss(return_per_sequence=False),
'clf_acc':
ltr_losses.TrackingClassificationAccuracy(threshold=0.25)
}
loss_weight = {
'test_clf': 1.0 * 500,
'test_clf_orig': 50,
'is_target': 0.1 * 500,
'is_target_after_prop': 0.1 * 500
}
dimp_jitter_fn = DiMPScoreJittering(distractor_ratio=0.1,
p_distractor=0.3,
max_distractor_enhance_factor=1.3,
min_distractor_enhance_factor=0.8)
actor = actors.KYSActor(net=net,
objective=objective,
loss_weight=loss_weight,
dimp_jitter_fn=dimp_jitter_fn)
optimizer = optim.Adam([{
'params': actor.net.predictor.parameters(),
'lr': 1e-2
}],
lr=1e-2)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=15,
gamma=0.2)
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer,
settings, lr_scheduler)
trainer.train(40, load_latest=True, fail_safe=True)
