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)
dataset_train = sampler.ATOMSampler([lasot_train, trackingnet_train], [1,3],
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=60, 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(150, load_latest=True, fail_safe=True)
#larget frame gap
#without coco
#lasot : trackingnet 1:3
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
# ]