def run(settings): settings.description = 'Default train settings for DiMP 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/iou', 'ClfTrain/clf_ce', 'ClfTrain/test_loss'] ''' Depth Inputs: 1) raw_depth X 2) norm_depth 3) centered_norm_depth 4) centered_raw_depth X 5) colormap 6) centered_colormap ''' # depth_inputs = 'norm_depth' # depth_inputs = 'colormap' depth_inputs = 'hha' # Train datasets # depthtrack_train = DepthTrack(root=settings.env.depthtrack_dir, split='train', dtype=depth_inputs) coco_train = MSCOCOSeq_depth(settings.env.cocodepth_dir, dtype=depth_inputs) # got10k_depth_train = MSCOCOSeq_depth(settings.env.got10kdepth_dir, dtype=depth_inputs) lasot_depth_train = Lasot_depth(root=settings.env.lasotdepth_dir, rgb_root=settings.env.lasot_dir, dtype=depth_inputs) # Validation datasets depthtrack_val = DepthTrack(root=settings.env.depthtrack_dir, split='val', dtype=depth_inputs) # 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 = { '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([coco_train, lasot_depth_train], [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([depthtrack_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.dimpnet50( filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5, # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # net = dimpnet.dimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=False, 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') # 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() }, { '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 DiMP with ResNet50 as backbone.' settings.batch_size = 4 settings.num_workers = 8 settings.multi_gpu = False settings.print_interval = 5 settings.normalize_mean = [0.485, 0.456, 0.406, 0] settings.normalize_std = [0.229, 0.224, 0.225, 1.0] 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') # Train datasets #lasot_train = Lasot(split='train') ptb_train = PrincetonRGBD(split='validation') # stc_train = StcRGBD(split='train') # kevinlai_train=kevinlaiRGBD(split='train') #trackingnet_train = TrackingNet(set_ids=list(range(11))) #coco_train = MSCOCOSeq() # Validation datasets #lasot_val = Lasot(split='train')#TrackingNet(set_ids=list(range(11,12))) ptb_val = PrincetonRGBD(split='validation') # 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([ptb_train], [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) dataset_val = sampler.DiMPSampler([ptb_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_rgbd_cls.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') # 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.conv_0.parameters(), 'lr': 0.1*5e-5}, #new layer {'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 0.1*5e-5}, {'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 0.1*5e-4}, {'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 0.01*5e-5}, {'params': actor.net.classifier.feature_extractor_depth.parameters(), 'lr': 0.1*5e-5}, {'params': actor.net.bb_regressor.parameters(), 'lr': 0.1*2e-4}, {'params': actor.net.feature_extractor.parameters(), 'lr': 0.01*2e-5}, {'params': actor.net.feature_extractor_depth.parameters(), 'lr': 0.1*2e-5}], lr=0.1*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(10, load_latest=True, fail_safe=True, path_pretrained=None)#'./checkpoints/dimp50.pth') #trainer.train(50, load_latest=True, fail_safe=True, path_pretrained=None) trainer.train(50, load_latest=True, fail_safe=True, path_pretrained='./checkpoints/dimp50.pth')