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
def data_get_cfg(dataset_base_name):
_C = CfgNode()
_C.DEBUG = False
_C.DEBUG_VIDEOS = []
# ['/disk1/mcs-data/intphys_scenes_dumped_perception/gravity_goal-0247',
# '/disk1/mcs-data/intphys_scenes_dumped_perception/gravity_goal-0489']
_C.MAX_VIDEOS = 10000
_C.VAL_VIDEOS = 130
_C.VAL_FRAMES = -1
_C.TEST_FRAMES = -1
_C.BASE_NAME = dataset_base_name #options intphys, adept, ai2thor-intphys
_C.REPROCESS_RAW_VIDEOS = False
_C.TRAINED_DETECTOR = CfgNode()
_C.TRAINED_DETECTOR.DO_INFERENCE = False
_C.TRAINED_DERENDER = CfgNode()
_C.TRAINED_DERENDER.DO_INFERENCE = False
_C.TRAINED_DERENDER.USE_INFERRED_BOXES = False
_C.SHAPESWORLD_JSON = CfgNode()
_C.SHAPESWORLD_JSON.REPROCESS = False
_C.ADEPT_JSON = CfgNode()
_C.ADEPT_JSON.REPROCESS = False
if _C.BASE_NAME == "intphys":
# _C.DATA_LOCATION = "/all/home/aldo/data/intphys_data/"
# _C.DATA_LOCATION = "/nobackup/users/aldopa/data/intphys.zip/"
_C.DATA_LOCATION = "/disk1/intphys_data"
_C.VAL_FRAMES = 1000
# _C.SPLITS = ("_val","_train")
# _C.SPLITS = ("_dev-meta_O1", "_dev-meta_O2", "_dev-meta_O3", "_val", "_train")
# _C.SPLITS = ("_dev-meta_O1", "_dev-meta_O2", "_dev-meta_O3", "_val")
_C.SPLITS = (
"_val",
# "_dev_O1", "_dev_O2", "_dev_O3",
# "_dev-meta_O1", "_dev-meta_O2", "_dev-meta_O3",
# "_test_O1", "_test_O2", "_test_O3",
"_train",
)
_C.SHAPESWORLD_JSON.FRAMES_RANGE_PER_VIDEO = (0, 100)
# _C.ATTRIBUTES_KEYS = ("attributes",
# "pred_attr_401469",
# "pred_attr_003227")
_C.ADEPT_JSON.VEL_DATA_ASSOC = "heuristic" # options: ground_truth, heuristic, None
_C.ADEPT_JSON.ATTRIBUTES_KEYS = (
# "attributes",
"pred_attr_82649",
"pred_attr_03227",
"pred_attr_06472",
"pred_attr_12664",
"pred_attr_22318",
"pred_attr_41337",
)
_C.MIN_AREA = 25
_C.TRAINED_DERENDER.EXP_DIR = "/all/home/aldo/cora-derenderer/output/intphys/derender/exp_00000/"
_C.TRAINED_DERENDER.ATTRIBUTES_WEIGHTS_MAP = CfgNode({
"pred_attr_82649":
"/all/home/aldo/cora-derenderer/output/intphys/derender/exp_00000/model_0082649.pth",
"pred_attr_03227":
"/all/home/aldo/cora-derenderer/output/intphys/derender/exp_00000/model_0003227.pth",
"pred_attr_06472":
"/all/home/aldo/cora-derenderer/output/intphys/derender/exp_00000/model_0006472.pth",
"pred_attr_12664":
"/all/home/aldo/cora-derenderer/output/intphys/derender/exp_00000/model_0012664.pth",
"pred_attr_22318":
"/all/home/aldo/cora-derenderer/output/intphys/derender/exp_00000/model_0022318.pth",
"pred_attr_41337":
"/all/home/aldo/cora-derenderer/output/intphys/derender/exp_00000/model_0041337.pth",
})
_C.TRAINED_DERENDER.USE_DEPTH = True
elif _C.BASE_NAME == "adept":
_C.DATA_LOCATION = "/all/home/aldo/data/adept_data"
# _C.DATA_LOCATION = "/nobackup/users/aldopa/data/adept.zip"
_C.SPLITS = (
# "_val",
# "_train",
"_human_create",
"_human_vanish",
"_human_short-overturn",
"_human_long-overturn",
"_human_visible-discontinuous",
"_human_invisible-discontinuous",
"_human_delay",
"_human_block",
)
_C.VAL_FRAMES = 1000
_C.MIN_AREA = 100
_C.TRAINED_DETECTOR.EXP_DIR = "output/adept/detector/exp_00011"
_C.TRAINED_DETECTOR.WEIGHTS_FILE = "output/adept/detector/exp_00011/model_0209999.pth"
_C.TRAINED_DERENDER.EXP_DIR = "/all/home/aldo/cora-derenderer/output/adept/derender/exp_00007"
_C.TRAINED_DERENDER.ATTRIBUTES_WEIGHTS_MAP = CfgNode({
# "pred_attr_00650": "/all/home/aldo/cora-derenderer/output/adept/derender/exp_00007/model_0000650.pth",
"pred_attr_43044":
"/all/home/aldo/cora-derenderer/output/adept/derender/exp_00007/model_0043044.pth",
# "pred_attr_10580": "/all/home/aldo/cora-derenderer/output/adept/derender/exp_00007/model_0010580.pth",
# "pred_attr_18377": "/all/home/aldo/cora-derenderer/output/adept/derender/exp_00007/model_0018377.pth",
# "pred_attr_34216": "/all/home/aldo/cora-derenderer/output/adept/derender/exp_00007/model_0034216.pth",
})
_C.TRAINED_DERENDER.USE_DEPTH = False
_C.SHAPESWORLD_JSON.FRAMES_RANGE_PER_VIDEO = (4, 124)
_C.ADEPT_JSON.VEL_DATA_ASSOC = "None" # options: ground_truth, heuristic, None
_C.ADEPT_JSON.ATTRIBUTES_KEYS = (
"attributes",
"pred_attr_43044",
)
elif _C.BASE_NAME == "ai2thor-intphys":
_C.DATA_LOCATION = "/disk1/mcs-data"
_C.SPLITS = (
"_train",
"_val",
)
_C.MIN_AREA = 50
else:
raise NotImplementedError
# _C.DATA_MODE = "zip" if _C.DATA_LOCATION.endswith(".zip") else "folder"
return _C
