def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'ATOM IoUNet with default settings according to the paper.'
settings.batch_size = 64
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.feature_sz = 18
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
# Train datasets
lasot_train = Lasot(settings.env.lasot_dir, split='train')
trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(11)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# Validation datasets
trackingnet_val = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(11,12)))
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05))
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
# The augmentation transform applied to the validation set (individually to each image in the pair)
transform_val = tfm.Transform(tfm.ToTensor(),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
# Data processing to do on the training pairs
proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 16, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]}
data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
transform=transform_train,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
transform=transform_val,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler([lasot_train, trackingnet_train, coco_train], [1,1,1],
samples_per_epoch=1000*settings.batch_size, max_gap=50, processing=data_processing_train)
# The loader for training
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)
# The sampler for validation
dataset_val = sampler.ATOMSampler([trackingnet_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=50,
processing=data_processing_val)
# The loader for validation
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)
# Create network and actor
net = atom_models.atom_resnet18(backbone_pretrained=True)
objective = nn.MSELoss()
actor = actors.AtomActor(net=net, objective=objective)
# Optimizer
optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2)
# Create trainer
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(50, load_latest=True, fail_safe=True)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'ATOM IoUNet with ResNet18 backbone and trained with vid, lasot, coco.'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 64 # Batch size
settings.num_workers = 4 # Number of workers for image loading
settings.normalize_mean = [0.485, 0.456, 0.406
] # Normalize mean (default ImageNet values)
settings.normalize_std = [0.229, 0.224,
0.225] # Normalize std (default ImageNet values)
settings.search_area_factor = 5.0 # Image patch size relative to target size
settings.feature_sz = 18 # Size of feature map
settings.output_sz = settings.feature_sz * 16 # Size of input image patches
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
settings.proposal_params = {
'min_iou': 0.1,
'boxes_per_frame': 16,
'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]
}
# Train datasets
vid_train = ImagenetVID()
lasot_train = Lasot(split='train')
coco_train = MSCOCOSeq()
# Validation datasets
got10k_val = Got10k(split='val')
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.05)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = dltransforms.Compose([
dltransforms.ToArrayAndJitter(0.2),
dltransforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
# The augmentation transform applied to the validation set (individually to each image in the pair)
transform_val = dltransforms.Compose([
dltransforms.ToArray(),
dltransforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
# Data processing to do on the training pairs
data_processing_train = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_train,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_val,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[vid_train, lasot_train, coco_train], [1, 1, 1],
samples_per_epoch=1000 * settings.batch_size,
max_gap=50,
processing=data_processing_train)
# The loader for training
train_loader = loader.LTRLoader('train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=4,
stack_dim=1)
# The sampler for validation
dataset_val = sampler.ATOMSampler([got10k_val], [
1,
],
samples_per_epoch=500 *
settings.batch_size,
max_gap=50,
processing=data_processing_val)
# The loader for validation
val_loader = loader.LTRLoader('val',
dataset_val,
training=False,
batch_size=settings.batch_size,
epoch_interval=5,
num_workers=4,
stack_dim=1)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
net = atom_resnet18(backbone_pretrained=True)
# Freeze backbone
state_dicts = net.state_dict()
for k in state_dicts.keys():
if 'feature_extractor' in k and "running" not in k:
state_dicts[k].stop_gradient = True
# Set objective
objective = fluid.layers.square_error_cost
# Create actor, which wraps network and objective
actor = actors.AtomActor(net=net, objective=objective)
# Set to training mode
actor.train()
# define optimizer and learning rate
gama = 0.2
lr = 1e-3
lr_scheduler = fluid.dygraph.PiecewiseDecay(
[15, 30, 45],
values=[lr, lr * gama, lr * gama * gama],
step=1000,
begin=0)
optimizer = fluid.optimizer.Adam(
parameter_list=net.bb_regressor.parameters(),
learning_rate=lr_scheduler)
trainer = LTRTrainer(actor, [train_loader, val_loader], optimizer,
settings, lr_scheduler)
trainer.train(40, load_latest=False, fail_safe=False)
def run(settings):
settings.description = 'Default train settings for FCOT with ResNet50 as backbone.'
settings.multi_gpu = True
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.clf_target_filter_sz = 4
settings.reg_target_filter_sz = 3
settings.feature_sz = 18
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_factor = {'train': 3, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.hinge_threshold = 0.05
settings.logging_file = 'fcot_log.txt'
# Train datasets
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=list(range(4)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# Validation datasets
got10k_val = Got10k(settings.env.got10k_dir, split='votval')
# Data transform
transform_joint = dltransforms.ToGrayscale(probability=0.05)
transform_train = torchvision.transforms.Compose([
dltransforms.ToTensorAndJitter(0.2),
torchvision.transforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
transform_val = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.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 = {
'min_iou': 0.1,
'boxes_per_frame': 8,
'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]
}
label_params = {
'feature_sz': settings.feature_sz,
'sigma_factor': output_sigma,
'kernel_sz': settings.clf_target_filter_sz
}
data_processing_train = processing_fcot.AnchorFreeProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
output_spatial_scale=72 / 288.,
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_train,
joint_transform=transform_joint)
data_processing_val = processing_fcot.AnchorFreeProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
output_spatial_scale=72 / 288.,
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_val,
joint_transform=transform_joint)
# Train sampler and loader
dataset_train = sampler.FCOTSampler(
[lasot_train, got10k_train, trackingnet_train, coco_train],
[settings.lasot_rate, 1, 1, 1],
samples_per_epoch=settings.samples_per_epoch,
max_gap=30,
num_test_frames=3,
num_train_frames=3,
processing=data_processing_train)
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.FCOTSampler([got10k_val], [1],
samples_per_epoch=5000,
max_gap=30,
num_test_frames=3,
num_train_frames=3,
processing=data_processing_val)
loader_val = LTRLoader('val',
dataset_val,
training=False,
batch_size=settings.batch_size,
epoch_interval=5,
num_workers=settings.num_workers,
shuffle=False,
drop_last=True,
stack_dim=1)
# Create network
net = fcotnet.fcotnet(
clf_filter_size=settings.clf_target_filter_sz,
reg_filter_size=settings.reg_target_filter_sz,
backbone_pretrained=True,
optim_iter=5,
norm_scale_coef=settings.norm_scale_coef,
clf_feat_norm=True,
clf_feat_blocks=0,
final_conv=True,
out_feature_dim=512,
optim_init_step=0.9,
optim_init_reg=0.1,
init_gauss_sigma=output_sigma * settings.feature_sz,
num_dist_bins=100,
bin_displacement=0.1,
mask_init_factor=3.0,
target_mask_act='sigmoid',
score_act='relu',
train_reg_optimizer=settings.train_reg_optimizer,
train_cls_72_and_reg_init=settings.train_cls_72_and_reg_init,
train_cls_18=settings.train_cls_18)
# Load dimp-model as initial weights
device = torch.device('cuda:{}'.format(settings.devices_id[0]) if torch.
cuda.is_available() else 'cpu')
if settings.use_pretrained_dimp:
assert settings.pretrained_dimp50 is not None
dimp50 = torch.load(settings.pretrained_dimp50, map_location=device)
state_dict = collections.OrderedDict()
for key, v in dimp50['net'].items():
if key.split('.')[0] == 'feature_extractor':
state_dict['.'.join(key.split('.')[1:])] = v
net.feature_extractor.load_state_dict(state_dict)
state_dict = collections.OrderedDict()
for key, v in dimp50['net'].items():
if key.split('.')[0] == 'classifier':
state_dict['.'.join(key.split('.')[1:])] = v
net.classifier_18.load_state_dict(state_dict)
print("loading backbone and Classifier modules from DiMP50 done.")
# Load fcot-model trained in the previous stage
if settings.load_model:
assert settings.fcot_model is not None
load_dict = torch.load(settings.fcot_model)
fcot_dict = net.state_dict()
load_fcotnet_dict = {
k: v
for k, v in load_dict['net'].items() if k in fcot_dict
}
fcot_dict.update(load_fcotnet_dict)
net.load_state_dict(fcot_dict)
print("loading FCOT model done.")
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, device_ids=settings.devices_id, dim=1).to(device)
# Loss for cls_72, cls_18 and regression
objective = {
'test_clf_72': ltr_losses.LBHinge(threshold=settings.hinge_threshold),
'test_clf_18': ltr_losses.LBHinge(threshold=settings.hinge_threshold),
'reg_72': REGLoss(dim=4)
}
# Create actor and adam-optimizer
if settings.train_cls_72_and_reg_init and settings.train_cls_18:
### train regression branch and clssification branches jointly, except for regression optimizer (TODO: fix)
print("train cls_72, cls_18 and reg_init jointly...")
loss_weight = {
'test_clf_72': 100,
'test_init_clf_72': 100,
'test_iter_clf_72': 400,
'test_clf_18': 100,
'test_init_clf_18': 100,
'test_iter_clf_18': 400,
'reg_72': 1
}
actor = actors.FcotActor(net=net,
objective=objective,
loss_weight=loss_weight,
device=device)
optimizer = optim.Adam(
[{
'params':
actor.net.classifier_72.filter_initializer.parameters(),
'lr': 5e-5
}, {
'params':
actor.net.classifier_72.filter_optimizer.parameters(),
'lr': 5e-4
}, {
'params':
actor.net.classifier_72.feature_extractor.parameters(),
'lr': 5e-5
}, {
'params':
actor.net.classifier_18.filter_initializer.parameters(),
'lr': 5e-5
}, {
'params':
actor.net.classifier_18.filter_optimizer.parameters(),
'lr': 5e-4
}, {
'params':
actor.net.classifier_18.feature_extractor.parameters(),
'lr': 5e-5
}, {
'params': actor.net.regressor_72.parameters()
}, {
'params': actor.net.pyramid_first_conv.parameters()
}, {
'params': actor.net.pyramid_36.parameters()
}, {
'params': actor.net.pyramid_72.parameters()
}, {
'params': actor.net.feature_extractor.parameters(),
'lr': 2e-5
}],
lr=2e-4)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[35, 46, 60],
gamma=0.2)
elif settings.train_cls_72_and_reg_init:
# Setting of the first training stage: train backbone, cls_72 and regression (except for regression optimizer) branch.
print("train cls_72 and reg_init...")
loss_weight = {
'test_clf_72': 100,
'test_init_clf_72': 10,
'test_iter_clf_72': 400,
'test_clf_18': 0,
'test_init_clf_18': 0,
'test_iter_clf_18': 0,
'reg_72': 0.3
}
actor = actors.FcotCls72AndRegInitActor(net=net,
objective=objective,
loss_weight=loss_weight,
device=device)
optimizer = optim.Adam(
[{
'params':
actor.net.classifier_72.filter_initializer.parameters(),
'lr': 5e-5
}, {
'params':
actor.net.classifier_72.filter_optimizer.parameters(),
'lr': 5e-4
}, {
'params':
actor.net.classifier_72.feature_extractor.parameters(),
'lr': 5e-5
}, {
'params': actor.net.regressor_72.parameters()
}, {
'params': actor.net.pyramid_first_conv.parameters()
}, {
'params': actor.net.pyramid_36.parameters()
}, {
'params': actor.net.pyramid_72.parameters()
}, {
'params': actor.net.feature_extractor.parameters(),
'lr': 2e-5
}],
lr=2e-4)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[35, 45, 69],
gamma=0.2)
elif settings.train_reg_optimizer:
# Setting of the second training stage: train regression optimizer.
print("train regression optimizer...")
loss_weight = {
'test_reg_72': 1,
'test_init_reg_72': 0,
'test_iter_reg_72': 1
}
actor = actors.FcotOnlineRegressionActor(net=net,
objective=objective,
loss_weight=loss_weight,
device=device)
optimizer = optim.Adam(
[{
'params': actor.net.regressor_72.filter_optimizer.parameters()
}],
lr=5e-4)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[2],
gamma=0.2)
elif settings.train_cls_18:
print("train cls_18...")
# Setting of the third training stage: train cls_18 branch.
loss_weight = {
'test_clf_18': 100,
'test_init_clf_18': 100,
'test_iter_clf_18': 400
}
actor = actors.FcotCls18Actor(net=net,
objective=objective,
loss_weight=loss_weight,
device=device)
optimizer = optim.Adam(
[{
'params':
actor.net.classifier_18.filter_initializer.parameters(),
'lr': 5e-5
}, {
'params':
actor.net.classifier_18.filter_optimizer.parameters(),
'lr': 5e-4
}, {
'params':
actor.net.classifier_18.feature_extractor.parameters(),
'lr': 5e-5
}],
lr=2e-4)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[25],
gamma=0.2)
else:
# TODO: train jointly
raise Exception("Please run training in correct way.")
trainer = LTRFcotTrainer(actor, [loader_train, loader_val],
optimizer,
settings,
device,
lr_scheduler,
logging_file=settings.logging_file)
trainer.train(settings.total_epochs, load_latest=True, fail_safe=True)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'SiamRPN with AlexNet backbone.'
settings.print_interval = 100 # How often to print loss and other info
settings.batch_size = 512 # Batch size
settings.samples_per_epoch = 600000 # Number of training pairs per epoch
settings.num_workers = 8 # Number of workers for image loading
settings.search_area_factor = {'train': 1.0, 'test': 2.0}
settings.output_sz = {'train': 127, 'test': 255}
settings.scale_type = 'context'
settings.border_type = 'meanpad'
# Settings for the image sample and label generation
settings.center_jitter_factor = {'train': 0.125, 'test': 2.0}
settings.scale_jitter_factor = {'train': 0.05, 'test': 0.18}
settings.label_params = {
'search_size': 255,
'output_size': 17,
'anchor_stride': 8,
'anchor_ratios': [0.33, 0.5, 1, 2, 3],
'anchor_scales': [8],
'num_pos': 16,
'num_neg': 16,
'num_total': 64,
'thr_high': 0.6,
'thr_low': 0.3
}
settings.loss_weights = {'cls': 1., 'loc': 1.2}
settings.neg = 0.2
# Train datasets
vos_train = YoutubeVOS()
vid_train = ImagenetVID()
coco_train = MSCOCOSeq()
det_train = ImagenetDET()
#lasot_train = Lasot(split='train')
#got10k_train = Got10k(split='train')
# Validation datasets
vid_val = ImagenetVID()
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.25)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_exemplar = dltransforms.Transpose()
transform_instance = dltransforms.Compose(
[
dltransforms.Color(probability=1.0),
dltransforms.Blur(probability=0.18),
dltransforms.Transpose()
])
transform_instance_mask = dltransforms.Transpose()
# Data processing to do on the training pairs
data_processing_train = processing.SiamProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
label_params=settings.label_params,
train_transform=transform_exemplar,
test_transform=transform_instance,
test_mask_transform=transform_instance_mask,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.SiamProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
label_params=settings.label_params,
transform=transform_exemplar,
joint_transform=transform_joint)
nums_per_epoch = settings.samples_per_epoch // settings.batch_size
# The sampler for training
dataset_train = sampler.MaskSampler(
[vid_train, coco_train, det_train, vos_train],
[2, 1, 1, 2],
samples_per_epoch=nums_per_epoch * settings.batch_size,
max_gap=100,
processing=data_processing_train,
neg=settings.neg)
# The loader for training
train_loader = loader.LTRLoader(
'train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
stack_dim=0)
# The sampler for validation
dataset_val = sampler.MaskSampler(
[vid_val],
[1, ],
samples_per_epoch=100 * settings.batch_size,
max_gap=100,
processing=data_processing_val)
# The loader for validation
val_loader = loader.LTRLoader(
'val',
dataset_val,
training=False,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
stack_dim=0)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
def scale_loss(loss):
total_loss = 0
for k in settings.loss_weights:
total_loss += loss[k] * settings.loss_weights[k]
return total_loss
net = SiamRPN_AlexNet(scale_loss=scale_loss)
# Define objective
objective = {
'cls': select_softmax_with_cross_entropy_loss,
'loc': weight_l1_loss,
}
# Create actor, which wraps network and objective
actor = actors.SiamActor(net=net, objective=objective)
# Define optimizer and learning rate
decayed_lr = fluid.layers.exponential_decay(
learning_rate=0.01,
decay_steps=nums_per_epoch,
decay_rate=0.9407,
staircase=True)
lr_scheduler = LinearLrWarmup(
learning_rate=decayed_lr,
warmup_steps=5*nums_per_epoch,
start_lr=0.005,
end_lr=0.01)
optimizer = fluid.optimizer.Adam(
parameter_list=net.rpn_head.parameters(),
learning_rate=lr_scheduler)
trainer = LTRTrainer(actor, [train_loader, val_loader], optimizer, settings, lr_scheduler)
trainer.train(50, load_latest=False, fail_safe=False)
def run(settings):
settings.description = 'Default train settings for PrDiMP with ResNet50 as backbone.'
settings.batch_size = 10
settings.num_workers = 8
settings.multi_gpu = False
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_factor = {'train': 3, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.hinge_threshold = 0.05
settings.print_stats = ['Loss/total', 'Loss/bb_ce', 'ClfTrain/clf_ce']
# Train datasets
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=list(range(4)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# 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 = {'boxes_per_frame': 128, 'gt_sigma': (0.05, 0.05), 'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]}
label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz}
label_density_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz, 'normalize': True}
data_processing_train = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
label_density_params=label_density_params,
transform=transform_train,
joint_transform=transform_joint)
data_processing_val = processing.KLDiMPProcessing(search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
label_density_params=label_density_params,
transform=transform_val,
joint_transform=transform_joint)
# Train sampler and loader
dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1],
samples_per_epoch=26000, max_gap=200, num_test_frames=3, num_train_frames=3,
processing=data_processing_train)
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.DiMPSampler([got10k_val], [1], samples_per_epoch=5000, max_gap=200,
num_test_frames=3, num_train_frames=3,
processing=data_processing_val)
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)
# Create network and actor
net = dimpnet.klcedimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5,
clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512,
optim_init_step=1.0, optim_init_reg=0.05, optim_min_reg=0.05,
gauss_sigma=output_sigma * settings.feature_sz, alpha_eps=0.05, normalize_label=True, init_initializer='zero')
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
objective = {'bb_ce': klreg_losses.KLRegression(), 'clf_ce': klreg_losses.KLRegressionGrid()}
loss_weight = {'bb_ce': 0.0025, 'clf_ce': 0.25, 'clf_ce_init': 0.25, 'clf_ce_iter': 1.0}
actor = tracking_actors.KLDiMPActor(net=net, objective=objective, loss_weight=loss_weight)
# Optimizer
optimizer = optim.Adam([{'params': actor.net.classifier.parameters(), 'lr': 1e-3},
{'params': actor.net.bb_regressor.parameters(), 'lr': 1e-3},
{'params': actor.net.feature_extractor.parameters(), 'lr': 2e-5}],
lr=2e-4)
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(50, load_latest=True, fail_safe=True)
def run(settings):
settings.description = 'Default train settings for training VOS with box initialization.'
settings.batch_size = 8
settings.num_workers = 4
settings.multi_gpu = False
settings.print_interval = 1
settings.normalize_mean = [102.9801, 115.9465, 122.7717]
settings.normalize_std = [1.0, 1.0, 1.0]
settings.feature_sz = (52, 30)
settings.output_sz = (settings.feature_sz[0] * 16,
settings.feature_sz[1] * 16)
settings.search_area_factor = 5.0
settings.crop_type = 'inside_major'
settings.max_scale_change = None
settings.device = "cuda:0"
settings.center_jitter_factor = {'train': 3, 'test': (5.5, 4.5)}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.min_target_area = 500
ytvos_train = YouTubeVOS(version="2019", multiobj=False, split='jjtrain')
ytvos_valid = YouTubeVOS(version="2019", multiobj=False, split='jjvalid')
coco_train = MSCOCOSeq()
# Data transform
transform_joint = tfm.Transform(tfm.ToBGR(),
tfm.ToGrayscale(probability=0.05),
tfm.RandomHorizontalFlip(probability=0.5))
transform_train = tfm.Transform(
tfm.ToTensorAndJitter(0.2, normalize=False),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
transform_val = tfm.Transform(
tfm.ToTensorAndJitter(0.0, normalize=False),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
data_processing_train = processing.LWLProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
crop_type=settings.crop_type,
max_scale_change=settings.max_scale_change,
transform=transform_train,
joint_transform=transform_joint,
new_roll=True)
data_processing_val = processing.LWLProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
crop_type=settings.crop_type,
max_scale_change=settings.max_scale_change,
transform=transform_val,
joint_transform=transform_joint,
new_roll=True)
# Train sampler and loader
dataset_train = sampler.LWLSampler([ytvos_train, coco_train], [1, 1],
samples_per_epoch=settings.batch_size *
1000,
max_gap=100,
num_test_frames=1,
num_train_frames=1,
processing=data_processing_train)
dataset_val = sampler.LWLSampler([ytvos_valid], [1],
samples_per_epoch=settings.batch_size *
100,
max_gap=100,
num_test_frames=1,
num_train_frames=1,
processing=data_processing_val)
loader_train = LTRLoader('train',
dataset_train,
training=True,
num_workers=settings.num_workers,
stack_dim=1,
batch_size=settings.batch_size)
loader_val = LTRLoader('val',
dataset_val,
training=False,
num_workers=settings.num_workers,
epoch_interval=5,
stack_dim=1,
batch_size=settings.batch_size)
net = lwt_box_networks.steepest_descent_resnet50(
filter_size=3,
num_filters=16,
optim_iter=5,
backbone_pretrained=True,
out_feature_dim=512,
frozen_backbone_layers=['conv1', 'bn1', 'layer1'],
label_encoder_dims=(16, 32, 64),
use_bn_in_label_enc=False,
clf_feat_blocks=0,
final_conv=True,
backbone_type='mrcnn',
box_label_encoder_dims=(
64,
64,
),
final_bn=False)
base_net_weights = network_loading.load_trained_network(
settings.env.workspace_dir,
'ltr/lwl/lwl_stage2/LWTLNet_ep0080.pth.tar')
# Copy weights
net.feature_extractor.load_state_dict(
base_net_weights.feature_extractor.state_dict())
net.target_model.load_state_dict(
base_net_weights.target_model.state_dict())
net.decoder.load_state_dict(base_net_weights.decoder.state_dict())
net.label_encoder.load_state_dict(
base_net_weights.label_encoder.state_dict())
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
objective = {
'segm': LovaszSegLoss(per_image=False),
}
loss_weight = {
'segm': 100.0,
'segm_box': 10.0,
'segm_train': 10,
}
actor = lwtl_actors.LWLBoxActor(net=net,
objective=objective,
loss_weight=loss_weight)
# Optimizer
optimizer = optim.Adam([{
'params': actor.net.box_label_encoder.parameters(),
'lr': 1e-3
}],
lr=2e-4)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=20,
gamma=0.2)
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer,
settings, lr_scheduler)
trainer.train(50, load_latest=True, fail_safe=True)
def run(settings):
settings.description = 'Default train settings for DiMP with ResNet18 as backbone.'
settings.batch_size = 26
settings.num_workers = 8
settings.multi_gpu = False
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_factor = {'train': 3, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.hinge_threshold = 0.05
# settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/init_loss', 'ClfTrain/test_loss']
# Train datasets
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=list(range(4)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# Validation datasets
got10k_val = Got10k(settings.env.got10k_dir, split='votval')
# Data transform
transform_joint = dltransforms.ToGrayscale(probability=0.05)
transform_train = torchvision.transforms.Compose([
dltransforms.ToTensorAndJitter(0.2),
torchvision.transforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
transform_val = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.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 = {
'min_iou': 0.1,
'boxes_per_frame': 8,
'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]
}
label_params = {
'feature_sz': settings.feature_sz,
'sigma_factor': output_sigma,
'kernel_sz': settings.target_filter_sz
}
data_processing_train = processing.DiMPProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_train,
joint_transform=transform_joint)
data_processing_val = processing.DiMPProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_val,
joint_transform=transform_joint)
# Train sampler and loader
dataset_train = sampler.DiMPSampler(
[lasot_train, got10k_train, trackingnet_train, coco_train],
[0.25, 1, 1, 1],
samples_per_epoch=26000,
max_gap=30,
num_test_frames=3,
num_train_frames=3,
processing=data_processing_train)
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.DiMPSampler([got10k_val], [1],
samples_per_epoch=5000,
max_gap=30,
num_test_frames=3,
num_train_frames=3,
processing=data_processing_val)
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)
# Create network and actor
net = dimpnet.dimpnet18(filter_size=settings.target_filter_sz,
backbone_pretrained=True,
optim_iter=5,
clf_feat_norm=True,
final_conv=True,
optim_init_step=0.9,
optim_init_reg=0.1,
init_gauss_sigma=output_sigma *
settings.feature_sz,
num_dist_bins=100,
bin_displacement=0.1,
mask_init_factor=3.0,
target_mask_act='sigmoid',
score_act='relu')
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
objective = {
'iou': nn.MSELoss(),
'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)
}
loss_weight = {
'iou': 1,
'test_clf': 100,
'test_init_clf': 100,
'test_iter_clf': 400
}
actor = actors.DiMPActor(net=net,
objective=objective,
loss_weight=loss_weight)
# Optimizer
optimizer = optim.Adam(
[{
'params': actor.net.classifier.filter_initializer.parameters(),
'lr': 5e-5
}, {
'params': actor.net.classifier.filter_optimizer.parameters(),
'lr': 5e-4
}, {
'params': actor.net.classifier.feature_extractor.parameters(),
'lr': 5e-5
}, {
'params': actor.net.bb_regressor.parameters(),
'lr': 1e-3
}, {
'params': actor.net.feature_extractor.parameters()
}],
lr=2e-4)
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(50, load_latest=True, fail_safe=True)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'distilled ATOM IoUNet with default settings according to the paper.'
settings.batch_size = 32
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.feature_sz = 18
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
# Train datasets
lasot_train = Lasot(settings.env.lasot_dir, split='train')
trackingnet_train = TrackingNet(settings.env.trackingnet_dir,
set_ids=list(range(11)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# Validation datasets
trackingnet_val = TrackingNet(settings.env.trackingnet_dir,
set_ids=list(range(11, 12)))
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05))
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = tfm.Transform(
tfm.ToTensorAndJitter(0.2),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
# The augmentation transform applied to the validation set (individually to each image in the pair)
transform_val = tfm.Transform(
tfm.ToTensor(),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
# Data processing to do on the training pairs
proposal_params = {
'min_iou': 0.1,
'boxes_per_frame': 16,
'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]
}
data_processing_train = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
transform=transform_train,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
transform=transform_val,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[lasot_train, trackingnet_train, coco_train], [1, 1, 1],
samples_per_epoch=1000 * settings.batch_size,
max_gap=50,
processing=data_processing_train)
# The loader for training
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)
# The sampler for validation
dataset_val = sampler.ATOMSampler([trackingnet_val], [1],
samples_per_epoch=500 *
settings.batch_size,
max_gap=50,
processing=data_processing_val)
# The loader for validation
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 teacher network
teacher_net = atom_models.atom_resnet18(backbone_pretrained=True)
teacher_path = '/home/ddanier/CFKD/pytracking/networks/atom_default.pth'
teacher_net = loading.load_weights(teacher_net, teacher_path, strict=True)
print(
'*******************Teacher net loaded successfully*******************'
)
# Create student network and actor
student_net = atom_models.atom_mobilenetsmall(backbone_pretrained=False)
##########################################################
### Distil backbone first, turn off grad for regressor ###
##########################################################
for p in student_net.bb_regressor.parameters():
p.requires_grad_(False)
objective = distillation.CFKDLoss(
reg_loss=nn.MSELoss(),
w_ts=0.,
w_ah=0.,
w_cf=0.01,
w_fd=100.,
cf_layers=['conv1', 'layer1', 'layer2', 'layer3'])
actor = actors.AtomCompressionActor(student_net, teacher_net, objective)
# Optimizer
optimizer = optim.Adam(actor.student_net.feature_extractor.parameters(),
lr=1e-2)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=15,
gamma=0.1)
# Create trainer
trainer = LTRDistillationTrainer(actor, [loader_train, loader_val],
optimizer, settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(50, load_latest=False, fail_safe=True)
########################################################
## Distil regressor next, turn off grad for backbone ###
########################################################
for p in trainer.actor.student_net.bb_regressor.parameters():
p.requires_grad_(True)
for p in trainer.actor.student_net.feature_extractor.parameters():
p.requires_grad_(False)
objective = distillation.CFKDLoss(reg_loss=nn.MSELoss(),
w_ts=1.,
w_ah=0.1,
w_cf=0.,
w_fd=0.)
trainer.actor.objective = objective
# Optimizer
trainer.optimizer = optim.Adam(
trainer.actor.student_net.bb_regressor.parameters(), lr=1e-2)
trainer.lr_scheduler = optim.lr_scheduler.StepLR(trainer.optimizer,
step_size=15,
gamma=0.1)
# Run training (set fail_safe=False if you are debugging)
trainer.train(100, load_latest=False, fail_safe=True)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'ATOM IoUNet with default settings.'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 64 # Batch size
settings.num_workers = 4 # Number of workers for image loading
settings.normalize_mean = [
0.485, 0.456, 0.406
] # Normalize mean (default pytorch ImageNet values)
settings.normalize_std = [
0.229, 0.224, 0.225
] # Normalize std (default pytorch ImageNet values)
settings.search_area_factor = 5.0 # Image patch size relative to target size
settings.feature_sz = 18 # Size of feature map
settings.output_sz = settings.feature_sz * 16 # Size of input image patches
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
settings.proposal_params = {
'min_iou': 0.1,
'boxes_per_frame': 16,
'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]
}
# Train datasets
lasot_train = Lasot(split='train')
trackingnet_train = TrackingNet(set_ids=list(range(11)))
coco_train = MSCOCOSeq()
# Validation datasets
trackingnet_val = TrackingNet(set_ids=list(range(11, 12)))
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.05)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = torchvision.transforms.Compose([
dltransforms.ToTensorAndJitter(0.2),
torchvision.transforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
# The augmentation transform applied to the validation set (individually to each image in the pair)
transform_val = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
# Data processing to do on the training pairs
data_processing_train = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_train,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_val,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[lasot_train, trackingnet_train, coco_train], [1, 1, 1],
samples_per_epoch=1800 * settings.batch_size,
max_gap=50,
processing=data_processing_train)
# The loader for training
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)
# The sampler for validation
dataset_val = sampler.ATOMSampler([trackingnet_val], [1],
samples_per_epoch=500 *
settings.batch_size,
max_gap=50,
processing=data_processing_val)
# The loader for validation
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)
# Create network
net = atom_models.atom_resnet50(backbone_pretrained=True)
# Set objective
objective = nn.MSELoss()
# Create actor, which wraps network and objective
actor = actors.AtomActor(net=net, objective=objective)
# Optimizer
optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3)
# Learning rate scheduler
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=15,
gamma=0.2)
# Create trainer
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer,
settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(40, load_latest=True, fail_safe=False)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'Siam selection for detection with default settings.'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 1 # Batch size
assert settings.batch_size==1,"only implement for batch_size 1"
settings.num_workers = 4 # Number of workers for image loading
settings.normalize_mean = [0.485, 0.456, 0.406] # Normalize mean (default pytorch ImageNet values)
settings.normalize_std = [0.229, 0.224, 0.225] # Normalize std (default pytorch ImageNet values)
settings.search_area_factor = 5.0 # Image patch size relative to target size
settings.feature_sz = 18 # Size of feature map
settings.output_sz = settings.feature_sz * 16 # Size of input image patches
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
settings.proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 16, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]}
# Train datasets
lasot_train = Lasot(split='train')
trackingnet_train = TrackingNet(set_ids=list(range(11)))
coco_train = MSCOCOSeq()
# Validation datasets
trackingnet_val = TrackingNet(set_ids=list(range(11,12)))
# # The joint augmentation transform, that is applied to the pairs jointly
# transform_joint = dltransforms.ToGrayscale(probability=0.05)
#
# # The augmentation transform applied to the training set (individually to each image in the pair)
# transform_train = torchvision.transforms.Compose([dltransforms.ToTensorAndJitter(0.2),
# torchvision.transforms.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)])
#
# # The augmentation transform applied to the validation set (individually to each image in the pair)
# transform_val = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
# torchvision.transforms.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)])
# Data processing to do on the training pairs
# data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor,
# output_sz=settings.output_sz,
# center_jitter_factor=settings.center_jitter_factor,
# scale_jitter_factor=settings.scale_jitter_factor,
# mode='sequence',
# proposal_params=settings.proposal_params,
# transform=transform_train,
# joint_transform=transform_joint)
#
# # Data processing to do on the validation pairs
# data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor,
# output_sz=settings.output_sz,
# center_jitter_factor=settings.center_jitter_factor,
# scale_jitter_factor=settings.scale_jitter_factor,
# mode='sequence',
# proposal_params=settings.proposal_params,
# transform=transform_val,
# joint_transform=transform_joint)
img_transform = ImageTransform(
size_divisor=32, mean=[123.675, 116.28, 103.53],std=[58.395, 57.12, 57.375],to_rgb=True)
data_processing=processing.SiamSelProcessing(transform=img_transform)
# The sampler for training
dataset_train = sampler.ATOMSampler([lasot_train, trackingnet_train, coco_train], [1,1,1],
samples_per_epoch=1000*settings.batch_size, max_gap=50*20,
processing=data_processing)
# The loader for training
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)
# The sampler for validation
dataset_val = sampler.ATOMSampler([trackingnet_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=50*20,
processing=data_processing)
# The loader for validation
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)
# Create network
# net = atom_models.atom_resnet18(backbone_pretrained=True)
net=SiamSelNet()
# Set objective
objective = nn.BCEWithLogitsLoss()
# Create actor, which wraps network and objective
actor = actors.SiamSelActor(net=net, objective=objective)
# Optimizer
optimizer = optim.Adam(actor.net.selector.parameters(), lr=1e-4,weight_decay=0.0001)
# Learning rate scheduler
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=40, gamma=0.2)
# lr_scheduler = WarmupMultiStepLR(optimizer,[50*1000,80*1000])
# Create trainer
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(100, load_latest=True, fail_safe=True)
#larget frame gap
def run(settings):
settings.description = 'Transformer-assisted tracker. Our baseline approach is SuperDiMP'
settings.batch_size = 40
settings.num_workers = 8
settings.multi_gpu = True
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 = 6.0
settings.output_sigma_factor = 1 / 4
settings.target_filter_sz = 4
settings.feature_sz = 22
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_factor = {'train': 3, 'test': 5.5}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.hinge_threshold = 0.05
# settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/init_loss', 'ClfTrain/test_loss']
# Train datasets
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=list(range(4)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# Validation datasets
got10k_val = Got10k(settings.env.got10k_dir, split='votval')
# Data transform
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05),
tfm.RandomHorizontalFlip(probability=0.5))
transform_train = tfm.Transform(
tfm.ToTensorAndJitter(0.2), tfm.RandomHorizontalFlip(probability=0.5),
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 = {
'boxes_per_frame': 128,
'gt_sigma': (0.05, 0.05),
'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)]
}
label_params = {
'feature_sz': settings.feature_sz,
'sigma_factor': output_sigma,
'kernel_sz': settings.target_filter_sz
}
label_density_params = {
'feature_sz': settings.feature_sz,
'sigma_factor': output_sigma,
'kernel_sz': settings.target_filter_sz
}
data_processing_train = processing.KLDiMPProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
crop_type='inside_major',
max_scale_change=1.5,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
label_density_params=label_density_params,
transform=transform_train,
joint_transform=transform_joint)
data_processing_val = processing.KLDiMPProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
crop_type='inside_major',
max_scale_change=1.5,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
label_density_params=label_density_params,
transform=transform_val,
joint_transform=transform_joint)
# Train sampler and loader
dataset_train = sampler.DiMPSampler(
[lasot_train, got10k_train, trackingnet_train, coco_train],
[1, 1, 1, 1],
samples_per_epoch=50000,
max_gap=500,
num_test_frames=3,
num_train_frames=3,
processing=data_processing_train)
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.DiMPSampler([got10k_val], [1],
samples_per_epoch=10000,
max_gap=500,
num_test_frames=3,
num_train_frames=3,
processing=data_processing_val)
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)
# Create network and actor
net = dimpnet.dimpnet50(
filter_size=settings.target_filter_sz,
backbone_pretrained=True,
optim_iter=5,
clf_feat_norm=True,
clf_feat_blocks=0,
final_conv=True,
out_feature_dim=512,
optim_init_step=0.9,
optim_init_reg=0.1,
init_gauss_sigma=output_sigma * settings.feature_sz,
num_dist_bins=100,
bin_displacement=0.1,
mask_init_factor=3.0,
target_mask_act='sigmoid',
score_act='relu',
frozen_backbone_layers=['conv1', 'bn1', 'layer1', 'layer2'])
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
objective = {
'bb_ce': klreg_losses.KLRegression(),
'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)
}
loss_weight = {
'bb_ce': 0.01,
'test_clf': 100,
'test_init_clf': 100,
'test_iter_clf': 400
}
actor = tracking_actors.KLDiMPActor(net=net,
objective=objective,
loss_weight=loss_weight)
# Optimizer
optimizer = optim.Adam(
[{
'params': actor.net.classifier.filter_initializer.parameters(),
'lr': 5e-5
}, {
'params': actor.net.classifier.filter_optimizer.parameters(),
'lr': 5e-4
}, {
'params': actor.net.classifier.feature_extractor.parameters(),
'lr': 5e-5
}, {
'params': actor.net.classifier.transformer.parameters(),
'lr': 1e-3
}, {
'params': actor.net.bb_regressor.parameters(),
'lr': 1e-3
}, {
'params': actor.net.feature_extractor.layer3.parameters(),
'lr': 2e-5
}],
lr=2e-4)
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(50, load_latest=True, fail_safe=True)
from ltr.dataset import MSCOCOSeq coco_train = MSCOCOSeq() train_frame_ids =[1,2,3] for i in range(148924,coco_train.get_num_sequences()): print(i) coco_train.get_frames(i, train_frame_ids)
def run(settings):
# Most common settings are assigned in the settings struct
settings.device = 'cuda'
settings.description = 'TransT with default settings.'
settings.batch_size = 38
settings.num_workers = 8
settings.multi_gpu = True
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 = 4.0
settings.template_area_factor = 2.0
settings.search_feature_sz = 32
settings.template_feature_sz = 16
settings.search_sz = settings.search_feature_sz * 8
settings.temp_sz = settings.template_feature_sz * 8
settings.center_jitter_factor = {'search': 3, 'template': 0}
settings.scale_jitter_factor = {'search': 0.25, 'template': 0}
# Transformer
settings.position_embedding = 'sine'
settings.hidden_dim = 256
settings.dropout = 0.1
settings.nheads = 8
settings.dim_feedforward = 2048
settings.featurefusion_layers = 4
# Train datasets
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=list(range(4)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05))
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
# Data processing to do on the training pairs
data_processing_train = processing.TransTProcessing(search_area_factor=settings.search_area_factor,
template_area_factor = settings.template_area_factor,
search_sz=settings.search_sz,
temp_sz=settings.temp_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
transform=transform_train,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.TransTSampler([lasot_train, got10k_train, coco_train, trackingnet_train], [1,1,1,1],
samples_per_epoch=1000*settings.batch_size, max_gap=100, processing=data_processing_train)
# The loader for training
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=0)
# Create network and actor
model = transt_models.transt_resnet50(settings)
# Wrap the network for multi GPU training
if settings.multi_gpu:
model = MultiGPU(model, dim=0)
objective = transt_models.transt_loss(settings)
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('number of params:', n_parameters)
actor = actors.TranstActor(net=model, objective=objective)
# Optimizer
param_dicts = [
{"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]},
{
"params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad],
"lr": 1e-5,
},
]
optimizer = torch.optim.AdamW(param_dicts, lr=1e-4,
weight_decay=1e-4)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 500)
# Create trainer
trainer = LTRTrainer(actor, [loader_train], optimizer, settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(1000, load_latest=True, fail_safe=True)
def run(settings):
# Most common settings are assigned in the settings struct
settings.base_model = ''
settings.description = 'SiamMask_sharp with ResNet-50 backbone.'
settings.print_interval = 100 # How often to print loss and other info
settings.batch_size = 64 # Batch size
settings.samples_per_epoch = 600000 # Number of training pairs per epoch
settings.num_workers = 8 # Number of workers for image loading
settings.search_area_factor = {'train': 1.0, 'test': 143. / 127.}
settings.output_sz = {'train': 127, 'test': 143}
settings.scale_type = 'context'
settings.border_type = 'meanpad'
# Settings for the image sample and label generation
settings.center_jitter_factor = {'train': 0.2, 'test': 0.4}
settings.scale_jitter_factor = {'train': 0.05, 'test': 0.18}
settings.label_params = {
'search_size': 143,
'output_size': 3,
'anchor_stride': 8,
'anchor_ratios': [0.33, 0.5, 1, 2, 3],
'anchor_scales': [8],
'num_pos': 16,
'num_neg': 16,
'num_total': 64,
'thr_high': 0.6,
'thr_low': 0.3
}
settings.loss_weights = {'cls': 0., 'loc': 0., 'mask': 1}
settings.neg = 0
# Train datasets
vos_train = YoutubeVOS()
coco_train = MSCOCOSeq()
# Validation datasets
vos_val = vos_train
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.25)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_exemplar = dltransforms.Transpose()
transform_instance = dltransforms.Compose([
dltransforms.Color(probability=1.0),
dltransforms.Blur(probability=0.18),
dltransforms.Transpose()
])
transform_instance_mask = dltransforms.Transpose()
# Data processing to do on the training pairs
data_processing_train = processing.SiamProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
label_params=settings.label_params,
train_transform=transform_exemplar,
test_transform=transform_instance,
test_mask_transform=transform_instance_mask,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.SiamProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
label_params=settings.label_params,
transform=transform_exemplar,
joint_transform=transform_joint)
nums_per_epoch = settings.samples_per_epoch // settings.batch_size
# The sampler for training
dataset_train = sampler.MaskSampler([coco_train, vos_train], [1, 1],
samples_per_epoch=nums_per_epoch *
settings.batch_size,
max_gap=100,
processing=data_processing_train,
neg=settings.neg)
# The loader for training
train_loader = loader.LTRLoader('train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
stack_dim=0)
# The sampler for validation
dataset_val = sampler.MaskSampler([vos_val], [
1,
],
samples_per_epoch=100 *
settings.batch_size,
max_gap=100,
processing=data_processing_val)
# The loader for validation
val_loader = loader.LTRLoader('val',
dataset_val,
training=False,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
stack_dim=0)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
def scale_loss(loss):
total_loss = 0
for k in settings.loss_weights:
total_loss += loss[k] * settings.loss_weights[k]
return total_loss
net = SiamMask_ResNet50_sharp(scale_loss=scale_loss)
# Load parameters from the best_base_model
if settings.base_model == '':
raise Exception(
'The base_model path is not setup. Check settings.base_model in "ltr/train_settings/siammask/siammask_res50_sharp.py".'
)
para_dict, _ = fluid.load_dygraph(settings.base_model)
model_dict = net.state_dict()
for key in model_dict.keys():
if key in para_dict.keys():
model_dict[key] = para_dict[key]
net.set_dict(model_dict)
# Define objective
objective = {
'cls': select_softmax_with_cross_entropy_loss,
'loc': weight_l1_loss,
'mask': select_mask_logistic_loss
}
# Create actor, which wraps network and objective
actor = actors.SiamActor(net=net, objective=objective)
# Set to training mode
actor.train()
# Define optimizer and learning rate
decayed_lr = fluid.layers.exponential_decay(learning_rate=0.0005,
decay_steps=nums_per_epoch,
decay_rate=0.9,
staircase=True)
lr_scheduler = LinearLrWarmup(learning_rate=decayed_lr,
warmup_steps=5 * nums_per_epoch,
start_lr=0.0001,
end_lr=0.0005)
optimizer = fluid.optimizer.Adam(
parameter_list=net.mask_head.parameters() +
net.refine_head.parameters(),
learning_rate=lr_scheduler)
trainer = LTRTrainer(actor, [train_loader, val_loader], optimizer,
settings, lr_scheduler)
trainer.train(20, load_latest=False, fail_safe=False)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'ATOM using the probabilistic maximum likelihood trained regression model for bounding-box' \
'regression presented in [https://arxiv.org/abs/1909.12297].'
settings.batch_size = 64
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.feature_sz = 18
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
# Train datasets
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=list(range(4)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# Validation datasets
got10k_val = Got10k(settings.env.got10k_dir, split='votval')
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05))
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = tfm.Transform(
tfm.ToTensorAndJitter(0.2),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
# The augmentation transform applied to the validation set (individually to each image in the pair)
transform_val = tfm.Transform(
tfm.ToTensor(),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
# Data processing to do on the training pairs
proposal_params = {
'boxes_per_frame': 128,
'gt_sigma': (0, 0),
'proposal_sigma': [(0.05, 0.05), (0.5, 0.5)],
'add_mean_box': True
}
data_processing_train = processing.KLBBregProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
transform=transform_train,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.KLBBregProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=proposal_params,
transform=transform_val,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[lasot_train, got10k_train, trackingnet_train, coco_train],
[1, 1, 1, 1],
samples_per_epoch=1000 * settings.batch_size,
max_gap=200,
processing=data_processing_train)
# The loader for training
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)
# The sampler for validation
dataset_val = sampler.ATOMSampler([got10k_val], [1],
samples_per_epoch=500 *
settings.batch_size,
max_gap=200,
processing=data_processing_val)
# The loader for validation
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)
# Create network and actor
net = atom_models.atom_resnet18(backbone_pretrained=True)
objective = klreg_losses.MLRegression()
actor = bbreg_actors.AtomBBKLActor(net=net, objective=objective)
# Optimizer
optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=15,
gamma=0.2)
# Create trainer
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer,
settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(50, load_latest=True, fail_safe=True)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'Siam selection for detection with default settings.'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 1 # Batch size
assert settings.batch_size==1,"only implement for batch_size 1"
settings.num_workers = 4 # Number of workers for image loading
settings.normalize_mean = [0.485, 0.456, 0.406] # Normalize mean (default pytorch ImageNet values)
settings.normalize_std = [0.229, 0.224, 0.225] # Normalize std (default pytorch ImageNet values)
settings.search_area_factor = 5.0 # Image patch size relative to target size
settings.feature_sz = 18 # Size of feature map
settings.output_sz = settings.feature_sz * 16 # Size of input image patches
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
settings.proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 16, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]}
# Train datasets
lasot_train = Lasot(split='train')
trackingnet_train = TrackingNet(set_ids=list(range(11)))
coco_train = MSCOCOSeq()
# Validation datasets
trackingnet_val = TrackingNet(set_ids=list(range(11,12)))
# # The joint augmentation transform, that is applied to the pairs jointly
# transform_joint = dltransforms.ToGrayscale(probability=0.05)
#
# # The augmentation transform applied to the training set (individually to each image in the pair)
# transform_train = torchvision.transforms.Compose([dltransforms.ToTensorAndJitter(0.2),
# torchvision.transforms.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)])
#
# # The augmentation transform applied to the validation set (individually to each image in the pair)
# transform_val = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
# torchvision.transforms.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)])
# Data processing to do on the training pairs
# data_processing_train = processing.ATOMProcessing(search_area_factor=settings.search_area_factor,
# output_sz=settings.output_sz,
# center_jitter_factor=settings.center_jitter_factor,
# scale_jitter_factor=settings.scale_jitter_factor,
# mode='sequence',
# proposal_params=settings.proposal_params,
# transform=transform_train,
# joint_transform=transform_joint)
#
# # Data processing to do on the validation pairs
# data_processing_val = processing.ATOMProcessing(search_area_factor=settings.search_area_factor,
# output_sz=settings.output_sz,
# center_jitter_factor=settings.center_jitter_factor,
# scale_jitter_factor=settings.scale_jitter_factor,
# mode='sequence',
# proposal_params=settings.proposal_params,
# transform=transform_val,
# joint_transform=transform_joint)
img_transform = ImageTransform(
size_divisor=32, mean=[123.675, 116.28, 103.53],std=[58.395, 57.12, 57.375],to_rgb=True)
data_processing=processing.SiamSelProcessing(transform=img_transform)
# The sampler for training
dataset_train = sampler.ATOMSampler([lasot_train, trackingnet_train, coco_train], [1,1,1],
samples_per_epoch=1000*settings.batch_size, max_gap=50,
processing=data_processing)
# The loader for training
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)
# The sampler for validation
dataset_val = sampler.ATOMSampler([trackingnet_val], [1], samples_per_epoch=500*settings.batch_size, max_gap=50,
processing=data_processing)
# The loader for validation
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)
# Create network
# net = atom_models.atom_resnet18(backbone_pretrained=True)
net=SiamSelNet()
# Set objective
objective = nn.BCEWithLogitsLoss()
# Create actor, which wraps network and objective
actor = actors.SiamSelActor(net=net, objective=objective)
# Optimizer
optimizer = optim.Adam(actor.net.selector.parameters(), lr=1e-4)
# Learning rate scheduler
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=40, gamma=0.2)
# lr_scheduler = WarmupMultiStepLR(optimizer,[50*1000,80*1000])
# Create trainer
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(100, load_latest=True, fail_safe=True)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
# def __init__(
# self,
# optimizer,
# milestones,
# gamma=0.1,
# warmup_factor=1.0 / 3,
# warmup_iters=500,
# warmup_method="linear",
# last_epoch=-1,
# ):
# if not list(milestones) == sorted(milestones):
# raise ValueError(
# "Milestones should be a list of" " increasing integers. Got {}",
# milestones,
# )
#
# if warmup_method not in ("constant", "linear"):
# raise ValueError(
# "Only 'constant' or 'linear' warmup_method accepted"
# "got {}".format(warmup_method)
# )
# self.milestones = milestones
# self.gamma = gamma
# self.warmup_factor = warmup_factor
# self.warmup_iters = warmup_iters
# self.warmup_method = warmup_method
# super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)
#
# def get_lr(self):
# warmup_factor = 1
# if self.last_epoch < self.warmup_iters:
# if self.warmup_method == "constant":
# warmup_factor = self.warmup_factor
# elif self.warmup_method == "linear":
# alpha = float(self.last_epoch) / self.warmup_iters
# warmup_factor = self.warmup_factor * (1 - alpha) + alpha
# return [
# base_lr
# * warmup_factor
# * self.gamma ** bisect_right(self.milestones, self.last_epoch)
# for base_lr in self.base_lrs
# ]