def parameters(ID=None): # Tracker specific parameters params = TrackerParams() # Parameters for debugging params.output_image = False params.output_image_path = './debug/result_image/' # Parameters for device and tracking model params.use_gpu = True params.device = 'cuda' params.model = deep.SBDTNet50(net_path='DCFST-50.pth') # Parameters for sampling search region params.search_padding = 5.0 # Sampling size relative to target size params.img_sample_area = 288**2 # Area of the search region image # Parameters for training locator params.regularization = 0.1 # Regularization term to train locator (train with 0.1) params.learning_rate = 0.016 # Learning rate to update locator params.output_sigma_factor = 1 / 4 # Standard deviation of Gaussian label relative to target size (train with 1/4) params.proposals_num = 31**2 # Number of uniform proposals in locator (train with 31**2) params.train_skipping = 10 # How often to run locator training (common: 10) params.target_not_found = 0.2 # Absolute score threshold to detect target missing (small) params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples # Parameters for hard negative samples mining params.hard_negative_mining = True # Whether to perform hard negative samples mining params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples (common: 0.5) params.hard_negative_learning_rate = 0.01 # Learning rate if hard negative samples are detected (small) params.hard_negative_distance_ratio = 0.75 # Scope to ignore the detection of hard negative samples relative to target size # Parameters for window params.window_output = True # Whether to perform window params.window_sigma_factor = 1.2 # Standard deviation of Gaussian window relative to target size (large) params.window_min_value = 0.5 # Min value of the output window (large) # Parameters for iounet refinement params.num_init_random_boxes = 19 # Number of random boxes for scale refinement (ATOM: 9) params.box_jitter_pos = 0.2 # How much to jitter the translation for random boxes (ATOM: 0.1) params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes (ATOM: 0.5) params.box_refinement_iter = 5 # Number of iterations for box refinement (ATOM: 5) params.maximal_aspect_ratio = 6 # Limit on the aspect ratio (ATOM: 6) params.iounet_k = 5 # Top-k average to estimate final box (ATOM: 3) params.scale_damp = 0.3 # Linear interpolation coefficient for target scale update (small) # Parameters for data augmentation params.augmentation = True # Whether to perform data augmentation params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.augmentation_method = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)] } return params
def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (18 * 16)**2 # Maximum image sample size params.min_image_sample_size = (18 * 16)**2 # Minimum image sample size params.search_area_scale = 5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.01 # Learning rate deep_params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples in memory deep_params.output_sigma_factor = 1 / 4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4, 4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = False # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones( 1 ) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2) } params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1 / 3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ( 'mlu', 0.05 ) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.distractor_threshold = 0.8 # Relative threshold to find distractors params.hard_negative_threshold = 0.5 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.8 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.use_iou_net = True # Use IoU net or not params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = ( 1e-2, 5e-2 ) # 1 # Gradient step length in the bounding box refinement 5e-3 2e-2 params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet18(net_path='atom_gmm_sampl', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 22 * 16 params.search_area_scale = 6 params.border_mode = 'inside_major' params.patch_max_scale_change = 1.5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = { 'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (2, 0.2) } params.augmentation_expansion_factor = 2 params.random_shift_factor = 1 / 3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='super_dimp.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' params.perform_hn_without_windowing = False params.save_sample_interval = 2 # save memory interval # [------ new parameters -------] # parameters for re-detection params.re_detection = True # [default:True] Re-detection params.flag_confidence = 6 # [default:6] different methods about confidence score params.cnt_global = 10000 # [default:10000->not use] global search for each count params.global_search_memory_limit = 200 # global search memory limit params.cnt_random = 5 # [default:5] random search for each count params.additional_candidate_random = 3 # [default:3] the number of candidates to search (when additional_candidate_adaptive is False) params.additional_candidate_adaptive = True # [default:True] adaptive number of candidates to search params.additional_candidate_adaptive_ratio = 0.1 # [default:0.1] ratio for adaptive number params.additional_candidate_adaptive_min = 1 # [default:1] minimum number for searching params.additional_candidate_adaptive_max = 10 # [default:10] maximum number for searching params.redetection_score_penalty = True # [default:True] score penalty for re-detection params.redetection_score_penalty_alpha = 0.75 # [default:0.75] score penalty parameter, about distance (big value -> more penalty) params.redetection_score_penalty_beta = 0.25 # [default:0.25] score penalty parameter, about time (small value -> slow detect) params.redetection_basic_penalty = 0.75 # [default:0.75] score penalty parameter, total score reduction, (0.75 -> 25% reduce score) params.redetection_now = True # [default:True] re-detection immediately after tracking failure params.no_update_early_redetection = 1 # no update period after re-detection success params.no_save_early_redetection = 0 # no save period after re-detection success params.redetection_global_search_flag = 1 # search position flag (0: 1/4 overlap, 1: half overlap, 2: none overlap) # parameters for more discriminative learning params.init_net_more_learn = True # more discriminative learning (init) params.init_more_learn_expand_searching_size = True # expanding searching size for more init samples params.init_more_learn_flag = 1 # search position flag (0: 1/4 overlap, 1: half overlap, 2: none overlap) params.init_more_learn_memory_limit = 40 # 100 -> 4Gb (smaller than sample_memory_size - 2), default:40 params.init_more_learn_no_transform = True # whether transformation params.init_more_sample_limit = 10 # searching number limit params.init_iou_more_learn = True # same as init_net_more_learn, iounet_augmentation = True -> can change params.track_net_more_learn = True # more discriminative learning (track) params.track_net_more_learn_search_flag = 2 # search position flag (0: 1/4 overlap, 1: half overlap, 2: none overlap) params.track_net_more_learn_cnt = 3 # more learning period params.track_net_more_learn_score = 0.70 # score condition for more learning params.track_net_more_learn_not_save = True # if True: not save and learn each time (not depend on track_net_more_learn_cnt) params.track_net_more_learn_save_weight = 0.5 # reduce memory weight (due to not real data) params.train_more_sample_limit = 10 # searching number limit params.additional_train_candidate = 2 # number of additional samples params.memory_weight_ratio = 2 # weight for initial feature (higher -> more important) params.init_blending = 0.0 # image blending with target and background (0: not blending, 0.1: 10% background, 90% target) # parameters for random erasing params.erasing_mode = True # Random erasing (RE) mode when tracking params.use_original_pos = False # Random erasing flag params.erasing_cnt = 5 # Random erasing period params.lower_scale = 0.02 # RE parameters params.upper_scale = 0.05 # RE parameters params.lower_ratio = 0.7 # RE parameters params.upper_ratio = 1.3 # RE parameters params.num_erasing = 10 # the number of random erasing images return params
def parameters(ID=None): # Tracker specific parameters params = TrackerParams() # ------------------ CHANGED ------------------# # Output result images params.output_image = False params.output_image_path = './debug/result_image/' # Training parameters for locator params.regularization = 0.1 # Regularization term to train locator params.learning_rate = 0.016 # Learning rate to update locator features model params.train_skipping = 10 # How often to run training (every n-th frame) params.output_sigma_factor = 1 / 4 # Standard deviation of Gaussian label relative to target size params.target_not_found_threshold = 0.25 # Absolute score threshold to detect target missing params.init_samples_minimum_weight = 0.25 # Minimum weight of initial samples # Hard negative samples mining params.hard_negative_mining = True # Perform hard negative samples mining params.hard_negative_threshold = 0.3 # Absolute threshold to find hard negative samples params.hard_negative_learning_rate = 0.125 # Learning rate if hard negative samples are detected params.hard_negative_distance_ratio = 0.15 # Detect hard negative samples range relative to image sample area # Windowing params.window_output = True # Perform windowing to output scores params.window_sigma_factor = 2.2 # Standard deviation of Gaussian output window relative to target size params.window_min = 0.8 # Min value of the output window # Scale update params.scale_damp = 0.3 # Linear interpolation coefficient for target scale update # Setup the tracking model params.model = deep.SBDTNet18(net_path='DCFST-18.pth') # GPU params.use_gpu = True params.device = 'cuda' # Patch sampling params.search_area_scale = 5 # Scale relative to target size params.img_sample_area = 288**2 # Area of the image sample # Locator proposals params.num_proposals_locator = 31**2 # Number of proposals in locator # Data augmentation params.augmentation = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)] } params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.use_augmentation = True # Whether to use augmentation # IoUNet params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 5 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Scale bounds params.min_scale_factor = 0.2 # Min value of the scale bound params.max_scale_factor = 5.0 # Max value of the scale bound return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 14 * 16 params.search_area_scale = 4 # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2) } params.augmentation_expansion_factor = 2 params.random_shift_factor = 1 / 3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' params.use_depth_channel = True return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18 * 16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True params.augmentation = { 'fliplr': True, 'rotate': [10, -10, 45, -45], 'blur': [(3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (2, 0.2) } params.augmentation_expansion_factor = 2 params.random_shift_factor = 1 / 3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 # params.net = NetWithBackbone(net_path='/home/sgn/Data1/yan/pytracking-models/checkpoints/ltr/dimp/dimp50_RGB/dimp50.pth', # use_gpu=params.use_gpu) # params.net = NetWithBackbone(net_path='/home/sgn/Data1/yan/pytracking-models/checkpoints/ltr/dimp/dimp50_DepthInputs_sigmoid/DiMPnet_ep0050.pth.tar', # use_gpu=params.use_gpu) params.net = NetWithBackbone( net_path= '/home/sgn/Data1/yan/pytracking-models/checkpoints/ltr/dimp/dimp50_D_CDTB_finetune_generated_PP_new02/DiMPnet_ep0150.pth.tar', use_gpu=params.use_gpu) # params.net = NetWithBackbone(net_path='/home/sgn/Data1/yan/pytracking-models/checkpoints/ltr/dimp/dimp50_DepthInputs_scratch_LaSOT_COCO/DiMPnet_ep0050.pth.tar', # use_gpu=params.use_gpu) # params.net = NetWithBackbone(net_path='/home/sgn/Data1/yan/pytracking-models/checkpoints/ltr/dimp/DOT50_Colormap_LaSOT_COCO_PretrainedDiMP_scratch/DiMPnet_ep0100.pth.tar', # use_gpu=params.use_gpu) # params.net = NetWithBackbone(net_path='/home/sgn/Data1/yan/pytracking-models/checkpoints/ltr/dimp/DOT50_Colormap_LaSOT_COCO_Got10k_scratch_PretrainedBackbone/DiMPnet_ep0050.pth.tar', # use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.use_classifier = True params.image_sample_size = 18 * 16 params.search_area_scale = 4.5 params.sample_memory_size = 50 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 20 params.init_train_frames = 5 params.update_classifier_and_regressor = True params.ues_select_sample_strategy = True # classifier-18 params.init_train_iter = 6 params.net_opt_iter = 5 params.net_opt_update_iter = 1 params.net_opt_hn_iter = 1 # classifier-72 params.init_train_iter_72 = 6 params.net_opt_iter_72 = 5 params.net_opt_update_iter_72 = 1 params.net_opt_hn_iter_72 = 1 # regressor params.reg_init_train_iter = 6 params.reg_net_opt_iter = 4 params.reg_net_opt_hn_iter = 0 params.reg_net_opt_update_iter = 1 params.lamda_72 = 1 params.lamda_18 = 1 params.reg_lamda = 0 params.merge_rate_72 = 0.2 params.merge_rate_18 = 0.8 params.use_augmentation = True params.augmentation = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], # 'dropout': (7, 0.2) } params.augmentation_expansion_factor = 2 params.random_shift_factor = 1 / 3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.12 params.distractor_threshold = 0.9 params.hard_negative_threshold = 0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.window_output = False params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True params.iou_select = False params.net = NetWithBackbone(net_path='fcot.pth', use_gpu=params.use_gpu) params.net.initialize() params.vot_anno_conversion_type = 'preserve_area' return params
def parameters(pth_path = None): params = TrackerParams() # These are usually set from outside params.debug = 1 # Debug level params.visualization = True # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (16 * 16) ** 2 # (18 * 16) ** 2 # Maximum image sample size params.min_image_sample_size = (16 * 16) ** 2 # (18 * 16) ** 2 # Minimum image sample size params.search_area_scale = 4.5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.0075 # Learning rate deep_params.output_sigma_factor = 1/4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4, 4) # Kernel size of filter deep_params.compressed_dim = 64 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = True # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones(1) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45,-45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3,1), (1, 3), (2, 2)], 'relativeshift': [(0.25, 0.25), (-0.25, 0.25), (0.25, -0.25), (-0.25, -0.25), (0.75, 0.75), (-0.75, 0.75), (0.75, -0.75), (-0.75, -0.75)]} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 0#1 / 3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'pca' # Method for initializing the projection matrix randn | pca params.filter_init_method = 'zeros' # Method for initializing the spatial filter randn | zeros params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ('mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = -1 # Absolute score threshold to detect target missing params.distractor_threshold = 100 # Relative threshold to find distractors params.hard_negative_threshold = 0.3 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.7 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) # use ResNet50 for filter params.use_resnet50 = True if params.use_resnet50: deep_feat_filter = deep.ATOMResNet50(output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) # deep_feat2 = deep.DRNetSE50(net_path='SE_Res50.pth', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features_filter = MultiResolutionExtractor([deep_feat_filter]) #params.features_filter = MultiResolutionExtractor([deep_feat2]) params.vot_anno_conversion_type = 'preserve_area' params.use_segmentation = True env = env_settings() net_path = env.network_path if pth_path is None: pth_path = '/home/jaffe/PycharmProjects//DMB/pytracking/networks/recurrent25.pth.tar' params.pth_path = pth_path params.segm_use_dist = True params.segm_normalize_mean = [0.485, 0.456, 0.406] params.segm_normalize_std = [0.229, 0.224, 0.225] params.segm_search_area_factor = 4.0 params.segm_feature_sz = 24 params.segm_output_sz = params.segm_feature_sz * 16 params.segm_scale_estimation = True params.segm_optimize_polygon = True params.tracking_uncertainty_thr = 3 params.response_budget_sz = 25 params.uncertainty_segm_scale_thr = 3.5 params.uncertainty_segment_thr = 10 params.segm_pixels_ratio = 2 params.mask_pixels_budget_sz = 25 params.segm_min_scale = 0.2 params.max_rel_scale_ch_thr = 0.75 params.consider_segm_pixels_ratio = 1 params.opt_poly_overlap_thr = 0.3 params.poly_cost_a = 1.2 params.poly_cost_b = 1 params.segm_dist_map_type = 'center' # center | bbox params.min_scale_change_factor = 0.95 params.max_scale_change_factor = 1.05 params.init_segm_mask_thr = 0.5 params.segm_mask_thr = 0.5 params.masks_save_path = '' # params.masks_save_path = 'save-masks-path' params.save_mask = False if params.masks_save_path != '': params.save_mask = True return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 14*16 params.search_area_scale = 4 params.border_mode = 'inside_major' params.patch_max_scale_change = 1.5 # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 # Detection parameters params.window_output = True # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [-5, 10, -30, 60], 'blur': [(2, 0.2), (1, 3)], 'relativeshift': [(0.6, 0.6), (-0.6, -0.6)], 'dropout': (3, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1/3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.45 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.box_refinement_space = 'relative' params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 2.5e-3 # 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) params.net = NetWithBackbone(net_path='trdimp_net.pth.tar', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True deep_params = TrackerParams() params.image_sample_size = 14 * 16 params.search_area_scale = 4 params.feature_size_odd = False # Learning parameters params.sample_memory_size = 250 deep_params.learning_rate = 0.0075 deep_params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 deep_params.output_sigma_factor = 1 / 4 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 params.scale_factors = torch.ones(1) # Spatial filter parameters deep_params.kernel_size = (4, 4) params.window_output = True # Detection parameters # params.score_upsample_factor = 1 # params.score_fusion_strategy = 'weightedsum' # deep_params.translation_weight = 1 # Init augmentation parameters # params.augmentation = {} params.augmentation = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2) } params.augmentation_expansion_factor = 2 params.random_shift_factor = 1 / 3 deep_params.use_augmentation = True # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.0 params.distractor_threshold = 100 params.hard_negative_threshold = 0.3 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.7 params.perform_hn_without_windowing = True params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = trackernet.SimpleTrackerResNet18( net_path='sdlearn_300_onlytestloss_lr_causal_mg30_iou_coco', fparams=deep_fparams) params.features = MultiResolutionExtractor([deep_feat]) params.vot_anno_conversion_type = 'preserve_area' return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 18 * 16 #18*16 params.search_area_scale = 5 # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.01 params.init_samples_minimum_weight = 0.25 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 10 #10 params.net_opt_update_iter = 2 params.net_opt_hn_iter = 1 params.update_classifier_initial = 5 params.update_classifier_initial_iter = 1 # Detection parameters params.window_output = False # Init augmentation parameters params.use_augmentation = True # params.augmentation = {'fliplr': True, # 'rotate': [10, -10, 45, -45], # 'blur': [(3,1), (1, 3), (2, 2)], # 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)], # 'dropout': (2, 0.2)} params.augmentation = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2) } params.augmentation_expansion_factor = 2 params.random_shift_factor = 1 / 3 # Advanced localization parameters params.advanced_localization = True params.target_not_found_threshold = 0.25 params.distractor_threshold = 0.8 #0.8 params.hard_negative_threshold = 0.5 #0.5 params.target_neighborhood_scale = 2.2 params.dispalcement_scale = 0.8 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True #redetection parameters params.num_history = 3 params.target_refound_threshold = params.target_not_found_threshold params.target_forcerefound_threshold = params.target_not_found_threshold + 0.03 #recover from redtection model, even the valid_d is false params.threshold_updatedepth = params.target_not_found_threshold + 0.05 params.frames_true_validd = 0 params.threshold_force_redetection = params.target_not_found_threshold - 0.05 params.threshold_allowupdateclassifer = params.target_not_found_threshold + 0.05 # IoUnet parameters params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.rotate_init_random_boxes = False params.net = NetWithBackbone(net_path='dimp50.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' #depth parameters params.use_depth_channel = True params.ptb_setting = True params.votd_setting = False params.stc_setting = False params.threshold_bhatta = 0.2 return params
def parameters(): params = TrackerParams() params.debug = 0 params.visualization = False params.use_gpu = True params.image_sample_size = 14*16 params.search_area_scale = 4 # Learning parameters params.sample_memory_size = 250 params.learning_rate = 0.0075 params.init_samples_minimum_weight = 0.0 params.train_skipping = 10 # Net optimization params params.update_classifier = True params.net_opt_iter = 25 params.net_opt_update_iter = 3 params.net_opt_hn_iter = 3 params.output_sigma_factor = 1/4 # Init augmentation parameters params.use_augmentation = True params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 params.random_shift_factor = 1 / 3 # localization parameters params.window_output = True params.use_clipped_window = True params.effective_search_area = 4.0 params.apply_window_to_dimp_score = True params.target_not_found_threshold_fused = 0.05 params.dimp_threshold = 0.05 params.reset_state_during_occlusion = True params.prev_feat_remove_subpixel_shift = True params.move_feat_to_center = True params.perform_hn_mining_dimp = True params.hard_negative_threshold = 0.5 params.target_neighborhood_scale_safe = 2.2 params.hard_negative_learning_rate = 0.02 params.update_scale_when_uncertain = True # IoUnet parameters params.use_iou_net = True params.iounet_augmentation = False params.iounet_use_log_scale = True params.iounet_k = 3 params.num_init_random_boxes = 9 params.box_jitter_pos = 0.1 params.box_jitter_sz = 0.5 params.maximal_aspect_ratio = 6 params.box_refinement_iter = 5 params.box_refinement_step_length = 1 params.box_refinement_step_decay = 1 params.remove_offset_in_fused_score = True params.score_downsample_factor = 1 params.net = NetWithBackbone(net_path='kys.pth', use_gpu=params.use_gpu) params.vot_anno_conversion_type = 'preserve_area' return params
def parameters(): params = TrackerParams() # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Patch sampling parameters params.max_image_sample_size = (320)**2 # Maximum image sample size params.min_image_sample_size = (320)**2 # Minimum image sample size params.search_area_scale = 5.5 # Scale relative to target size params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa # Optimization parameters params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 100 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 10 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.0075 # Learning rate deep_params.output_sigma_factor = [ 1 / 3, 1 / 4 ] # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 10 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = [(4, 4), (4, 4)] # Kernel size of filter deep_params.compressed_dim = [32, 32] # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = True # Perform windowing of output scores # Detection parameters params.scale_factors = torch.ones( 1 ) # What scales to use for localization (only one scale if IoUNet is used) params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = { 'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2) } params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1 / 3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ( 'mlu', 0.05 ) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = -1 # Absolute score threshold to detect target missing params.distractor_threshold = 100 # Relative threshold to find distractors params.hard_negative_threshold = 0.3 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.9 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.0075 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close params.alpha = 0 params.beta = -1 # Setup the feature extractor (which includes the IoUNet)i ATOMnet_ep0026.pth deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.DRNetMobileNetSmall(net_path='drnet_cfkd.pth.tar', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) params.vot_anno_conversion_type = 'preserve_area' return params
def parameters(): params = TrackerParams() # ++++++++++++++++++++++++++++ Parallel SiamMask +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ params.use_parallel_smask = True params.use_area_preserve = True params.parallel_smask_iou_threshold = 0.7 params.parallel_smask_area_preserve_threshold = 2 params.parallel_smask_config = osp.join(ROOT_DIR, 'pytracking/tracker/siamesemask/experiments/siammask/config_vot.json') params.parallel_smask_ckpt = osp.join(ROOT_DIR, 'pytracking/networks/SiamMask_VOT_LD.pth') params.use_smask_replace_atom = True # ++++++++++++++++++++++++++++ Sequential SiamMask +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ params.use_sequential_smask = True params.sequential_smask_ratio = 0.25 params.sequential_smask_config = osp.join(ROOT_DIR, 'pytracking/tracker/siamesemask_127/experiments/siammask/config_vot.json') params.sequential_smask_ckpt = osp.join(ROOT_DIR, 'pytracking/networks/SiamMask_VOT_LD.pth') # ++++++++++++++++++++++++++++ Refine ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ params.is_refine = False # use optimization algorithm to optimize mask params.is_fast_refine = False params.is_faster_refine = True params.angle_state = False params.soft_angle_state = False # ++++++++++++++++++++++++++++ ATOM PARAMS +++++++++++++++++++++++++++++++++++++++++++++++++++++ # Patch sampling parameters using area ratio params.use_adaptive_maximal_aspect_ratio = True params.use_area_ratio_adaptive_search_region = True params.area_ratio_adaptive_ratio = 0.005 params.use_area_ratio_prevent_zoom_in = True params.area_ratio_zoom_in_ratio = 0.75 params.feature_size_odd = False # Patch sampling parameters using current and mean max response speed params.use_speed_adaptive_search_region = True params.current_speed_threshold = 0.25 params.mean_speed_threshold = 0.20 params.center_distance_threshold = 0.3 # These are usually set from outside params.debug = 0 # Debug level params.visualization = False # Do visualization # Use GPU or not (IoUNet requires this to be True) params.use_gpu = True # Feature specific parameters deep_params = TrackerParams() # Optimization parameters params.CG_iter = 8 # The number of Conjugate Gradient iterations in each update after the first frame params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated) params.post_init_CG_iter = 0 # CG iterations to run after GN params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction # Learning parameters for each feature type deep_params.learning_rate = 0.0075 # Learning rate deep_params.output_sigma_factor = 1 / 4 # Standard deviation of Gaussian label relative to target size # Training parameters params.sample_memory_size = 250 # Memory size params.train_skipping = 5 # How often to run training (every n-th frame) # Online model parameters deep_params.kernel_size = (4, 4) # Kernel size of filter deep_params.compressed_dim = 768 # Dimension output of projection matrix deep_params.filter_reg = 1e-1 # Filter regularization factor deep_params.projection_reg = 1e-4 # Projection regularization factor # Windowing params.feature_window = False # Perform windowing of features params.window_output = True # Perform windowing of output scores # Detection parameters params.scale_factors = torch.Tensor([1.04 ** x for x in [-2, -1, 0, 1, 2]]) # Multi scale Test params.score_upsample_factor = 1 # How much Fourier upsampling to use # Init data augmentation parameters params.augmentation = {'fliplr': True, 'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60], 'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)], 'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)], 'dropout': (7, 0.2)} params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation params.random_shift_factor = 1 / 3 # How much random shift to do on each augmented sample deep_params.use_augmentation = True # Whether to use augmentation for this feature # Factorized convolution parameters # params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation params.update_projection_matrix = True # Whether the projection matrix should be optimized or not params.proj_init_method = 'randn' # Method for initializing the projection matrix params.filter_init_method = 'randn' # Method for initializing the spatial filter params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu') params.response_activation = ( 'mlu', 0.05) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu') # Advanced localization parameters params.advanced_localization = True # Use this or not params.target_not_found_threshold = -1 # Absolute score threshold to detect target missing params.distractor_threshold = 100 # Relative threshold to find distractors params.hard_negative_threshold = 0.3 # Relative threshold to find hard negative samples params.target_neighborhood_scale = 2.2 # Target neighborhood to remove params.dispalcement_scale = 0.7 # Dispacement to consider for distractors params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected params.update_scale_when_uncertain = True # Update scale or not if distractor is close # IoUNet parameters params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector params.iounet_k = 3 # Top-k average to estimate final box params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes params.maximal_aspect_ratio = 6 # Limit on the aspect ratio params.box_refinement_iter = 10 # Number of iterations for refining the boxes params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay) # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) deep_feat = deep.ATOMResNet50(net_path='atom_vid_lasot_coco_resnet50_fpn_ATOMnet_ep0040.pth.tar', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) params.features = MultiResolutionExtractor([deep_feat]) params.vot_anno_conversion_type = 'preserve_area' return params