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 # ]