# Whenever an argument can be either used for training or for testing, the # corresponding name will be post-fixed by a _TRAIN for a training parameter, # or _TEST for a test-specific parameter. # For example, the number of images during training will be # IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be # IMAGES_PER_BATCH_TEST # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() # The version number, to upgrade from old configs to new ones if any # changes happen. It's recommended to keep a VERSION in your config file. _C.VERSION = 2 _C.MODEL = CN() _C.MODEL.LOAD_PROPOSALS = False _C.MODEL.MASK_ON = False _C.MODEL.KEYPOINT_ON = False _C.MODEL.DEVICE = "cuda" _C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" # Path (possibly with schema like catalog:// or detectron2://) to a checkpoint file # to be loaded to the model. You can find available models in the model zoo. _C.MODEL.WEIGHTS = "" # Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR). # To train on images of different number of channels, just set different mean & std. # Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
def get_adept_cfg(dataset_basename):
_C = CfgNode()
_C.TYPE = "adept"
_C.DEBUG = False
_C.VERSION = "paper-adept" #options: "paper-adept", "intphys-adept"
_C.RESUME = False
_C.RESUME_DIR = ""
_C.SEED = -1
_C.CUDNN_BENCHMARK = False
_C.DEBUG_VIDEOS = [
] #["output/intphys/.data_tmp/adept_jsons/pred_attr_82649/intphys_dev_O1/video_00054.json"]
_C.ANALYZE_RESULTS_FOLDER = "None"
#"output/intphys/adept/bk_distributed_exp"
#"/home/aldo/cora-derender/output/adept/adept/bk_distributed_exp"
#"/all/home/aldo/cora-derenderer/output/adept/adept/distributed_exp/"
#'/all/home/aldo/cora-derenderer/output/intphys/adept/distributed_exp_complete_bk' #"None" #"output/intphys/adept/exp_00025"
# -----------------------------------------------------------------------------
# Datasets
# -----------------------------------------------------------------------------
_C.DATASETS = CfgNode()
_C.DATASETS.BASE_NAME = dataset_basename
if _C.DATASETS.BASE_NAME == "intphys":
####### INTPHYS ###########
_C.ATTRIBUTES_KEYS = (
"pred_attr_82649",
# "pred_attr_03227",
# "pred_attr_06472",
# "pred_attr_12664",
# "pred_attr_22318",
# "pred_attr_41337",
)
_C.DATASETS.TEST = (
"intphys_dev_O1",
"intphys_dev_O2",
"intphys_dev_O3",
# "intphys_dev-meta_O1",
# "intphys_dev-meta_O2",
# "intphys_dev-meta_O3"
)
elif _C.DATASETS.BASE_NAME == "adept":
_C.ATTRIBUTES_KEYS = (
# "attributes",
"pred_attr_43044",
# "pred_attr_00650",
# 'pred_attr_10580',
# "pred_attr_18377",
# "pred_attr_34216"
)
_C.DATASETS.TEST = (
# "adept_val",
# "adept_train", #TODO get back to only test
"adept_human_create",
"adept_human_vanish",
"adept_human_short-overturn",
"adept_human_long-overturn",
"adept_human_visible-discontinuous",
"adept_human_invisible-discontinuous",
"adept_human_delay",
"adept_human_block",
)
# -----------------------------------------------------------------------------
# Model
# -----------------------------------------------------------------------------
_C.MODEL = CfgNode()
_C.MODEL.META_ARCHITECTURE = "PARTICLE_FILTER"
# Particles to be used in the particle filter
_C.MODEL.N_PARTICLES = 128
# Threshold for minimal area for an objects to be considered visible
_C.MODEL.AREA_THRESHOLD = 200.
# -----------------------------------------------------------------------------
# Dynamics Model
# -----------------------------------------------------------------------------
_C.MODEL.STEP = CfgNode()
_C.MODEL.STEP.PERTURBATION = CfgNode()
# Whether to perturb the objects
_C.MODEL.STEP.PERTURBATION.TO_PERTURB = True
# Sigma in the velocity term
_C.MODEL.STEP.PERTURBATION.VELOCITY_SIGMA = [.01, .06]
_C.MODEL.STEP.PERTURBATION.SCALE_SIGMA = .0005
# Sigma in the location term
_C.MODEL.STEP.PERTURBATION.LOCATION_SIGMA = [.01, .06]
# Sigma in the velocity term, multiplicative
_C.MODEL.STEP.PERTURBATION.VELOCITY_LAMBDA = [.01, .06]
# -----------------------------------------------------------------------------
# Magic in the dynamics model
# -----------------------------------------------------------------------------
_C.MODEL.STEP.MAGIC = CfgNode()
# Whether to use magic
_C.MODEL.STEP.MAGIC.USE_MAGIC = True
# The probability to disappear
_C.MODEL.STEP.MAGIC.DISAPPEAR_PROBABILITY = .02
# The penalty for magically disappearing
_C.MODEL.STEP.MAGIC.DISAPPEAR_PENALTY = 10.
# The probability for magically stopping
_C.MODEL.STEP.MAGIC.STOP_PROBABILITY = .02
# The penalty for magically stopping
_C.MODEL.STEP.MAGIC.STOP_PENALTY = 1.
# The probability for magically accelerating
_C.MODEL.STEP.MAGIC.ACCELERATE_PROBABILITY = .04
# The penalty for magically accelerating
_C.MODEL.STEP.MAGIC.ACCELERATE_PENALTY = 1.
# The magnitude for magically accelerating
_C.MODEL.STEP.MAGIC.ACCELERATE_LAMBDA = 1.5
# -----------------------------------------------------------------------------
# Particle filter
# -----------------------------------------------------------------------------
# The period for particle filter to resample
_C.MODEL.RESAMPLE = CfgNode()
# Resample every period
_C.MODEL.RESAMPLE.PERIOD = 1
# Scaling on nll
_C.MODEL.RESAMPLE.FACTOR = 1.
# -----------------------------------------------------------------------------
# Mass sampler
# -----------------------------------------------------------------------------
_C.MODEL.MASS = CfgNode()
# Whether to sample mass
_C.MODEL.MASS.TO_SAMPLE_MASS = False
# The log mean of mass
_C.MODEL.MASS.LOG_MASS_MU = 0
# The log stdev of mass
_C.MODEL.MASS.LOG_MASS_SIGMA = 1
# -----------------------------------------------------------------------------
# Observation Model
# -----------------------------------------------------------------------------
_C.MODEL.UPDATING = CfgNode()
_C.MODEL.UPDATING.MATCHED = CfgNode()
# Loss for matched object updating
_C.MODEL.UPDATING.MATCHED.LOSS = "Smoothed_L_Half"
# Sigma in the location term
_C.MODEL.UPDATING.MATCHED.LOCATION_SIGMA = .2
# Sigma in the velocity term
_C.MODEL.UPDATING.MATCHED.VELOCITY_SIGMA = .2
_C.MODEL.UPDATING.MATCHED.SCALE_SIGMA = .05
_C.MODEL.UPDATING.UNMATCHED_BELIEF = CfgNode()
# Base Penalty coefficient for unseen object
_C.MODEL.UPDATING.UNMATCHED_BELIEF.BASE_PENALTY = 1.
# Penalty coefficient for unseen object w.r.t. mask area shown
_C.MODEL.UPDATING.UNMATCHED_BELIEF.MASK_PENALTY = .0001
_C.MODEL.UPDATING.UNMATCHED_OBSERVATION = CfgNode()
# Penalty for object appearing
_C.MODEL.UPDATING.UNMATCHED_OBSERVATION.PENALTY = .02
_C.MODEL.UPDATING.UNMATCHED_OBSERVATION.MAX_PENALTY = 12.
_C.MODEL.MATCHER = CfgNode()
# PENALTY FOR MISMATCHED OBJECT TYPES, ONLY BETWEEN OCCLUDER AND OTHER
_C.MODEL.MATCHER.TYPE_PENALTY = 10.
# PENALTY FOR MISMATCHED OBJECT COLOR
_C.MODEL.MATCHER.COLOR_PENALTY = 12.
# PENALTY FOR MISMATCHED OBJECT WHEN THEY ARE AFAR
_C.MODEL.MATCHER.DISTANCE_PENALTY = 14. if _C.VERSION == "intphys-adept" else 20.
# THE THRESHOLD FOR OBJECT BEING AFAR
_C.MODEL.MATCHER.DISTANCE_THRESHOLD = 2.
# THE BASE PENALTY BETWEEN PLACEHOLDER AND OBJECTS
_C.MODEL.MATCHER.BASE_PENALTY = 8.
# when more than 5 objects creating more objects should not happen
_C.MODEL.MATCHER.BASE_PENALTY_HIGH = 16
return _C
