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 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 = '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 = 'ATOM IoUNet with default settings, but additionally using GOT10k for training.' 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) coco_train = MSCOCOSeq_depth(settings.env.cocodepth_dir, dtype='rgbcolormap') lasot_depth_train = Lasot_depth(root=settings.env.lasotdepth_dir, dtype='rgbcolormap') depthtrack_train = DepthTrack(root=settings.env.depthtrack_dir, dtype='rgbcolormap') depthtrack_horizontal_train = DepthTrack( root=settings.env.depthtrack_horizontal_dir, dtype='rgbcolormap') depthtrack_vertical_train = DepthTrack( root=settings.env.depthtrack_vertical_dir, dtype='rgbcolormap') # Validation datasets # got10k_val = Got10k(settings.env.got10k_dir, split='votval') cdtb_val = CDTB(settings.env.cdtb_dir, split='val', dtype='rgbcolormap') # 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_depth_train, depthtrack_train, depthtrack_horizontal_train, depthtrack_vertical_train, coco_train ], [1, 1, 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([cdtb_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_DeT( backbone_pretrained=True, merge_type='max') # 'mean', 'conv', 'weightedSum' objective = nn.MSELoss() actor = actors.AtomActor(net=net, objective=objective) # Optimizer # optimizer = optim.Adam(actor.net.bb_regressor.parameters(), lr=1e-3) optimizer = optim.Adam( [ { 'params': actor.net.bb_regressor.parameters() }, { 'params': actor.net.feature_extractor.parameters(), 'lr': 2e-5 }, { 'params': actor.net.feature_extractor_depth.parameters(), 'lr': 2e-5 }, # {'params': actor.net.merge_layer2.parameters(), 'lr': 2e-5}, # {'params': actor.net.merge_layer3.parameters(), 'lr': 2e-5}, ], 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(80, load_latest=True, fail_safe=True)