def _init_transform(self,
transform=transforms.ToArray(),
train_transform=None,
test_transform=None,
train_mask_transform=None,
test_mask_transform=None,
joint_transform=None):
self.transform = {
'train': transform if train_transform is None else train_transform,
'test': transform if test_transform is None else test_transform,
'joint': joint_transform
}
super().__init__(transform=transform,
train_transform=train_transform,
test_transform=test_transform,
joint_transform=joint_transform)
self.transform['train_mask'] = self.transform['train'] if train_mask_transform is None \
else train_mask_transform
self.transform['test_mask'] = self.transform['test'] if test_mask_transform is None \
else test_mask_transform
def __init__(self,
transform=transforms.ToArray(),
train_transform=None,
test_transform=None,
joint_transform=None):
"""
args:
transform - The set of transformations to be applied on the images. Used only if train_transform or
test_transform is None.
train_transform - The set of transformations to be applied on the train images. If None, the 'transform'
argument is used instead.
test_transform - The set of transformations to be applied on the test images. If None, the 'transform'
argument is used instead.
joint_transform - The set of transformations to be applied 'jointly' on the train and test images. For
example, it can be used to convert both test and train images to grayscale.
"""
self.transform = {
'train': transform if train_transform is None else train_transform,
'test': transform if test_transform is None else test_transform,
'joint': joint_transform
}
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):
# Most common settings are assigned in the settings struct
settings.description = 'SiamFC with Alexnet backbone and trained with vid'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 8 # Batch size
settings.num_workers = 8 # Number of workers for image loading
settings.normalize_mean = [0., 0., 0.] # Normalize mean
settings.normalize_std = [1 / 255., 1 / 255., 1 / 255.] # Normalize std
settings.search_area_factor = {
'train': 1.0,
'test': 2.0078740157480315
} # roughly the same as SiamFC
settings.output_sz = {'train': 127, 'test': 255}
settings.scale_type = 'context'
settings.border_type = 'replicate'
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 0}
settings.scale_jitter_factor = {'train': 0, 'test': 0.}
# Train datasets
vid_train = ImagenetVID()
# Validation datasets
got10k_val = Got10k(split='val')
# 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.Compose([
dltransforms.ToArray(),
dltransforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
transform_instance = dltransforms.Compose([
DataAug(),
dltransforms.ToArray(),
dltransforms.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std)
])
# Data processing to do on the training pairs
data_processing_train = processing.SiamFCProcessing(
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',
train_transform=transform_exemplar,
test_transform=transform_instance,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.SiamFCProcessing(
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',
transform=transform_exemplar,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler([vid_train], [
1,
],
samples_per_epoch=6650 *
settings.batch_size,
max_gap=100,
processing=data_processing_train)
# 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=1)
# The sampler for validation
dataset_val = sampler.ATOMSampler([got10k_val], [
1,
],
samples_per_epoch=1000 *
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,
epoch_interval=5,
stack_dim=1)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
net = siamfc_alexnet()
# Create actor, which wraps network and objective
actor = actors.SiamFCActor(net=net,
objective=None,
batch_size=settings.batch_size,
shape=(17, 17),
radius=16,
stride=8)
# Set to training mode
actor.train()
# define optimizer and learning rate
lr_scheduler = fluid.layers.exponential_decay(learning_rate=0.01,
decay_steps=6650,
decay_rate=0.8685,
staircase=True)
regularizer = fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0005)
optimizer = fluid.optimizer.Momentum(momentum=0.9,
regularization=regularizer,
parameter_list=net.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)
