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