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
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.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 = 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) envs = EnvSettings() # Setup the feature extractor (which includes the IoUNet) deep_fparams = FeatureParams(feature_params=[deep_params]) <<<<<<< HEAD deep_feat = deep.DepthResNet50(net_path='depth/depth/', output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) ======= deep_feat = deep.ATOMResNet18(net_path=envs.checkpoints_path, output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) # deep_feat = deep.DepthResNet50(net_path=envs.checkpoints_path, output_layers=['layer3'], fparams=deep_fparams, normalize_power=2) >>>>>>> 9350f30c7faa82d7ba81152d0272f03b0103ee23 params.features = MultiResolutionExtractor([deep_feat]) return params